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    "content": "## The Problem: Two Worlds, One Tool\n\nClaude Code stores its configuration in a hidden `.claude` directory in your home folder. This single directory holds everything: your authentication tokens, settings, plugins, conversation history, and more. The assumption baked into this design is that you are one person with one account.\n\nBut enterprise users live in two worlds:\n\n- **Personal**: Direct Anthropic API access via OAuth, using your Claude Pro/Max subscription\n- **Work**: Access through corporate gateways (Portkey, AWS Bedrock, internal proxies) with SSO authentication\n\nWhen I wanted to switch from personal coding on a weekend to work on Monday, I had to manually juggle configuration files, clear cached tokens, and pray nothing broke. There had to be a better way.\n\n## [Understanding Claude Code's Architecture](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#understanding-claude-codes-architecture)\n\nBefore explaining the solution, let me walk you through how Claude Code actually stores its state. This is useful for anyone trying to debug authentication issues or customize their setup.\n\n``` ~/.claude/ | |-- .credentials.json      # OAuth tokens for personal account |-- settings.json          # User preferences and API helpers\n    |-- gateway-helper-config.json  # Enterprise gateway configuration | |-- /tokens/               # Enterprise SSO tokens (encrypted)\n    |   |-- jwt-*.enc          # JWT tokens for each SSO provider | |-- /plugins/              # Installed plugins\n    |   |-- config.json        # Plugin registry |   |-- /repos/            # Plugin source code | |-- /projects/             # Per-project state and memory\n    |-- /logs/                 # Gateway and session logs |-- /todos/                # Task lists from sessions\n    |-- history.jsonl          # Command history ```\n\n### [The Key Files](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#the-key-files)\n\n**`.credentials.json`** \\- This is where personal OAuth tokens live. When you authenticate with `claude login`, the access token, refresh token, expiration time, and subscription type get stored here:\n\n``` { \"claudeAiOauth\": { \"accessToken\": \"sk-ant-oat01-...\", \"refreshToken\": \"sk-ant-ort01-...\", \"expiresAt\": 1762386054209,\n    \"scopes\": [\"user:inference\", \"user:profile\"], \"subscriptionType\": \"max\" } } ```\n\n**`settings.json`** \\- This controls Claude Code's behavior. The critical field for enterprise users is `apiKeyHelper`, which points to a script that provides authentication tokens dynamically:\n\n``` { \"apiKeyHelper\": \"node \\\"path/to/jwt-token-helper.js\\\"\", \"statusLine\": { \"type\": \"command\", \"command\": \"...\"\n  } } ```\n\n**`/tokens/jwt-*.enc`** \\- Enterprise SSO tokens are stored encrypted. On macOS they go into Keychain, on Windows into Credential Manager, and on Linux/WSL they use AES-256-GCM encrypted files. This is important: your enterprise credentials never sit in plain text.\n\n## [The Solution: claude-switch](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#the-solution-claude-switch)\n\nThe solution to the dual-account problem is `claude-switch` \\- a shell script I wrote that handles the complete context switch between personal and corporate profiles. It does more than just swap authentication; it manages the entire development environment.\n\nHere is what `claude-switch` handles:\n\n1. **Claude authentication** \\- Swaps `settings.json` and `gateway-helper-config.json` between profiles\n2. **Plugin and MCP server sync** \\- Keeps your extensions consistent across both profiles\n3. **NPM registry** \\- Switches between corporate Artifactory and public npmjs.org\n4. **Token management** \\- Backs up and restores encrypted SSO tokens for each profile\n\n### [The Profile Structure](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#the-profile-structure)\n\n`claude-switch` stores profile configurations in a dedicated directory:\n\n``` ~/.claude/profiles/ |-- personal-settings.json      # Personal Claude settings |-- corporate-settings.json     # Corporate Claude settings\n    |-- corporate-gateway-config.json   # Gateway configuration |-- tokens-personal/            # Backed up personal tokens\n    |-- tokens-corporate/           # Backed up corporate tokens |-- npmrc-backup-personal       # Personal npm config\n    |-- npmrc-backup-corporate      # Corporate npm config ```\n\n### [Automatic Plugin Sync](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#automatic-plugin-sync)\n\nOne of the most useful features is automatic plugin and MCP server synchronization. When you install a new plugin or configure an MCP server in one profile, `claude-switch` merges it into both profiles on the next switch.\n\nThe sync uses a simple Python script embedded in the shell script:\n\n```python\n# Merge enabledPlugins (union of both)\npersonal_plugins = personal.get('enabledPlugins', {}) corporate_plugins = corporate.get('enabledPlugins', {})\nmerged_plugins = {**personal_plugins, **corporate_plugins}\n\n# Merge mcpServers (union of both)\npersonal_mcp = personal.get('mcpServers', {}) corporate_mcp = corporate.get('mcpServers', {}) merged_mcp = {**personal_mcp, **corporate_mcp}\n```\n\nThis means I never have to manually configure the same plugin twice. Install it once, switch profiles, and it is already there.\n\n### [NPM Registry Switching](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#npm-registry-switching)\n\nThis was the feature I did not expect to need but now cannot live without. Many enterprises use private npm registries (Artifactory, Nexus, etc.) for internal packages and security scanning. When I switch to corporate mode, `claude-switch` also switches my npm registry:\n\n```bash\n# Corporate mode\nnpm config set registry \"https://artifacts.example.com/artifactory/api/npm/npm-all/\"\n\n# Personal mode\nnpm config delete registry  # Falls back to public npmjs.org ```\n\nThis prevents the common problem of accidentally publishing to the wrong registry or failing to install packages because you are pointing at the wrong source.\n\n## [The Switch Flow](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#the-switch-flow)\n\nMy workflow is now a single command:\n\n### [Switching to Corporate](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#switching-to-corporate)\n\n```bash $ claude-switch corporate\n\nSwitching to CORPORATE Profile\n\n[1/4] Backing up current configuration... Backed up personal tokens\n\n[2/4] Syncing plugins and MCP servers... Synced enabledPlugins across profiles Synced mcpServers across profiles\n\n[3/4] Switching Claude authentication... Loaded corporate settings Configured corporate gateway\n\n[4/4] Switching NPM registry... NPM registry: Corporate Artifactory\n\nCORPORATE Profile Active\n\nClaude Code:\n  Gateway: https://live.ai.example.com Auth: Corporate SSO (gateway-helper) Billing: Corporate account\n\nNPM:\n  Registry: Corporate Artifactory\n\nNext: Run claude to start ```\n\n### [Switching to Personal](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#switching-to-personal)\n\n```bash $ claude-switch personal\n\nSwitching to PERSONAL Profile\n\n[1/4] Backing up current configuration... Backed up corporate tokens\n\n[2/4] Syncing plugins and MCP servers... Synced enabledPlugins across profiles Synced mcpServers across profiles\n\n[3/4] Switching Claude authentication... Loaded personal settings Removed corporate gateway config\n\n[4/4] Switching NPM registry... NPM registry: Public npmjs.org\n\nPERSONAL Profile Active\n\nClaude Code:\n  Auth: Direct Anthropic authentication Billing: Your personal Claude subscription\n\nNPM:\n  Registry: Public npmjs.org ```\n\n### [Checking Current Status](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#checking-current-status)\n\nRunning `claude-switch` without arguments shows the current state:\n\n```bash $ claude-switch\n\nCurrent Profile Status\n\nClaude Profile: CORPORATE Gateway: portkey URL: https://live.ai.example.com Auth: Corporate SSO\n\nNPM Registry: CORPORATE Registry: Corporate Artifactory\n\nUsage: claude-switch [corporate|personal]\n\n  corporate  Switch to corporate gateway + Artifactory personal   Switch to personal Anthropic account + public npm\n```\n\nThe beauty is that both profiles can coexist. The script does not touch `.credentials.json` when switching, and personal Claude does not touch `gateway-helper-config.json`. They are completely isolated.\n\n## [Token Storage Security](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#token-storage-security)\n\nA common concern with SSO workflows is: where do my credentials actually live?\n\nThe gateway helper uses platform-native secure storage:\n\nPlatform | Storage Method macOS | Keychain Access Windows | Windows Credential Manager Linux/WSL | Encrypted files (~/.claude/tokens/)\nFor Linux and WSL, tokens are encrypted with AES-256-GCM using a user-specific derived key. No external dependencies like `libsecret` are required. This was a deliberate design choice: we wanted it to work on minimal server environments and corporate-locked machines.\n\n## [Troubleshooting Common Issues](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#troubleshooting-common-issues)\n\nAfter using this setup for months, here are the issues I have hit and how to fix them:\n\n### [\"Authentication failed\" after it was working](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#authentication-failed-after-it-was-working)\n\nSSO tokens expire. Usually the gateway helper auto-refreshes, but if something goes wrong:\n\n```bash\n# Clear cached tokens (Linux/WSL)\nrm -rf ~/.claude/tokens/\n\n# Switch back to corporate to re-authenticate\nclaude-switch corporate ```\n\n### [Claude Code works but uses the wrong account](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#claude-code-works-but-uses-the-wrong-account)\n\nCheck which profile is active:\n\n```bash claude-switch ```\n\nThis shows exactly which profile is loaded and which npm registry is configured.\n\n### [Plugins missing after switching](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#plugins-missing-after-switching)\n\nIf plugins are not syncing, ensure Python3 is installed. The sync script requires it:\n\n```bash\n# Check if Python3 is available\npython3 --version\n\n# If not installed on Ubuntu/Debian\nsudo apt install python3 ```\n\n### [NPM installing from wrong registry](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#npm-installing-from-wrong-registry)\n\nAfter switching profiles, verify your npm registry:\n\n```bash npm config get registry ```\n\nIf it shows the wrong registry, run `claude-switch` again to reset it.\n\n## [Why This Matters](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#why-this-matters)\n\nThe broader point here is about **developer experience in the age of AI tooling**. As AI coding assistants become essential infrastructure, the friction of authentication and configuration will determine adoption.\n\nEnterprise security teams rightfully want control over how AI tools access corporate resources. But if the developer experience is too painful, people will find workarounds - personal accounts on corporate machines, copy-pasting code into web interfaces, or just avoiding AI tools altogether.\n\n`claude-switch` shows there is a middle path: keep enterprise security intact while making the developer experience seamless. Authentication tokens are managed automatically, plugins stay in sync, and even the npm registry follows along. Switching contexts is a single command that takes two seconds.\n\n## [Conclusion](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#conclusion)\n\nIf you are using Claude Code at an enterprise and struggling with authentication, here is my advice:\n\n1. **Understand the file structure** \\- Know where Claude Code stores state so you can debug issues\n2. **Build a profile switcher** \\- Do not manually swap config files; automate it with a script like `claude-switch`\n3. **Sync your extensions** \\- Plugins and MCP servers should follow you across profiles automatically\n4. **Do not forget npm** \\- If your enterprise uses a private registry, include that in your switching logic\n\nThe goal is to make the AI tool disappear into your workflow. You should not be thinking about authentication or configuration; you should be thinking about the code you are writing. A few hours spent setting up proper infrastructure pays dividends every single day.\n\n## [Get the Code](https://darshshah.xyz/posts/claude-code-at-work-solving-enterprise-authentication\\#get-the-code)\n\nThe full `claude-switch` script is open source and available on GitHub:\n\n**[github.com/darshshah981/claude-profile-manager](https://github.com/darshshah981/claude-profile-manager)**\n\nQuick install:\n\n```bash curl -o ~/.local/bin/claude-switch https://raw.githubusercontent.com/darshshah981/claude-profile-manager/main/claude-switch\nchmod +x ~/.local/bin/claude-switch claude-switch init ```\n\nThe script is configurable - edit `~/.claude/profiles/config.sh` to customize profile names, gateway URLs, npm registries, and which features to enable. The patterns here are portable across different AI coding assistants; it is really about wrapping any CLI tool with proper enterprise profile management.",
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    "content": "## The Tollbooth at Risk\n\nMeta’s economic model is deceptively simple. In 2024, it generated **$164.5 billion in revenue**, with **$41.39 billion** from advertising in Q1 2025 alone. Every dollar rested on Meta’s ability to mediate discovery: consumers don’t go directly to a retailer or service; they encounter it in their feed or through an ad slot embedded in a social graph.\n\nBut discovery is no longer captive. By mid-2025, OpenAI reported **2.5 billion daily prompts** flowing through ChatGPT. Surveys show **34% of U.S. adults** (and **58% of those under 30**) have used it. Google Search still dwarfs that scale at ~14 billion daily queries, but the delta is narrowing quickly. Agents are intercepting intent at the source, long before it would have touched a social feed.\n\nThe analogy is clear: just as mobile displaced desktop—not because it was technically superior, but because it was closer to the user’s moment of need—agents are displacing feeds. They are even closer to intent, collapsing the space between thought and action. For Meta, the risk is existential: if user intent no longer passes through its feeds, its tollbooth on attention collapses.\n\n## [The Vertical Defense](https://darshshah.xyz/posts/meta-defending-the-interface-by-owning-the-stack\\#the-vertical-defense)\n\nMeta’s strategy is not about building “a chatbot.” It is a systemic reconfiguration of its entire stack—compute, models, and distribution—to ensure it can own the new interface on its own terms.\n\n### [**1\\. Infrastructure**](https://darshshah.xyz/posts/meta-defending-the-interface-by-owning-the-stack\\#1-infrastructure)\n\nMeta is making capital expenditures on a scale rivaling sovereign wealth funds: **$64–72 billion in 2025**, much of it earmarked for AI infrastructure. By the end of 2024, it had deployed ~600,000 H100-equivalent GPUs, including 350,000 actual Nvidia H100s, and began shipping its **MTIA v2 inference chips**. A training chip is targeted for 2026.\n\nWhy does this matter? In AI, iteration speed compounds. A firm with surplus compute can train longer, test more variations, and refresh models faster. Meta’s independence from cloud bottlenecks gives it a structural advantage: its own learning curve. Unlike startups that rent GPUs or depend on Microsoft/AWS, Meta builds permanent, reusable infrastructure that compounds with time.\n\n### [**2\\. Models**](https://darshshah.xyz/posts/meta-defending-the-interface-by-owning-the-stack\\#2-models)\n\nUnlike OpenAI or Anthropic, Meta does not monetize models directly. Instead, it releases **Llama models as open weights**, with the latest reaching **2 trillion parameters** under its community license. This has two effects:\n\n- **Ecosystem shaping**: by making Meta’s architectures the “default,” it seeds the developer ecosystem in ways that later feed back into its products.\n\n- **Margin compression**: companies like OpenAI depend on API revenue; Meta undercuts them by making access effectively free. For Meta, models are not the product. They are complements to the product: advertising.\n\nThis echoes Microsoft’s strategy in the 1990s—where giving away Internet Explorer was less about browser revenue than protecting Windows. For Meta, open-weight models are defensive infrastructure.\n\n### [**3\\. Distribution**](https://darshshah.xyz/posts/meta-defending-the-interface-by-owning-the-stack\\#3-distribution)\n\nMeta’s assistant is now available to roughly **1 billion users** across WhatsApp, Instagram, and Messenger. Unlike standalone apps like ChatGPT, this is not about launching a chatbot but embedding an assistant into the very fabric of Meta’s communication surfaces.\n\nThis distribution brings three reinforcing advantages:\n\n- **Behavioral data**: Meta gains proprietary, real-time training signals from billions of interactions.\n\n- **Frictionless adoption**: users don’t need to download or subscribe; the assistant just appears.\n\n- **Platform stickiness**: once agents are integrated into chats, groups, and feeds, intent begins and ends inside Meta’s walled garden.\n\nIf agents are the new browsers, Meta is trying to pre-install itself at global scale before alternatives gain traction.\n\n## [The Quiet Transformation: From Ads to Outcomes](https://darshshah.xyz/posts/meta-defending-the-interface-by-owning-the-stack\\#the-quiet-transformation-from-ads-to-outcomes)\n\nPerhaps the most profound change is not technological but economic. For two decades, Meta sold **ad slots and targeting precision**. Advertisers supplied creative, budgets, and attribution systems.\n\nAI allows Meta to invert this arrangement. As Zuckerberg recently summarized: _“You come to us, state your objective, connect your bank account—we handle the rest.”_ Instead of selling impressions, Meta is pivoting to selling **outcomes**.\n\nThis matters for three reasons:\n\n1. **Small businesses** can now market with no expertise. They state “I want 100 sales” or “maximize my leads,” and Meta runs automated campaigns through Advantage+ systems.\n\n2. **Large enterprises** become more dependent on Meta’s opaque attribution engines. If only Meta knows how spend maps to outcomes, switching costs rise dramatically.\n\n3. **Revenue model shift**: outcome pricing captures more value. Advertisers pay for business results, not just eyeballs.\n\nEarly reports show **22% improvements in ROAS** (Return on Ad Spend) for campaigns run through Advantage+ versus manual campaigns. But this also raises tension: advertisers dislike black-box attribution, and regulators may demand transparency.\n\nStill, structurally, this shift tilts power further toward the platform that controls **both demand origination and measurement**—exactly what Meta is optimizing to be.\n\n## [Leadership and Culture](https://darshshah.xyz/posts/meta-defending-the-interface-by-owning-the-stack\\#leadership-and-culture)\n\nMeta’s ability to execute this pivot at scale rests on **Mark Zuckerberg’s unique leadership style**. Unlike many founders of trillion-dollar firms, Zuckerberg remains deeply involved in product detail and long-term vision. He has a track record of bold pivots:\n\n- Betting early on mobile.\n- Acquiring Instagram and WhatsApp.\n- Doubling down on VR/AR despite skepticism.\n  - Now, framing AI not as a side business but as an existential defense.\n\nThis founder-driven conviction helps explain why Meta can scale infrastructure at record speed, push open-weight models into the world, and integrate assistants into billion-user platforms. Few organizations of its size move so quickly.\n\nBut there is tension. Reports suggest Meta is paying nine-figure compensation packages to attract top AI researchers, yet several leading scientists have reportedly declined offers, citing cultural fit and independence concerns. Meta’s urgency and financial firepower do not automatically translate into intellectual alignment. For Meta to mature into a genuine **intellectual hub**, it must balance speed with credibility.\n\n## [Capital and Option Value](https://darshshah.xyz/posts/meta-defending-the-interface-by-owning-the-stack\\#capital-and-option-value)\n\nBeneath the strategy lies sheer financial muscle. In Q1 2025 alone, Meta generated **$41.39 billion in ad revenue** with **49% net income growth**. This cash machine allows it to fund long-term bets without compromising quarterly performance.\n\nIts **$14.3 billion investment for a 49% stake in Scale AI** secures scarce annotation capacity, effectively denying competitors access. Meanwhile, its **Superintelligence Labs** consolidate frontier research, giving Meta option value in a volatile technological landscape.\n\nFrom a corporate finance perspective, this is textbook strategy: preserve the right to act on future scenarios while keeping costs amortized across today’s revenue engine. Meta is treating AI as a portfolio of options rather than a binary bet.\n\n## [The Tensions Ahead](https://darshshah.xyz/posts/meta-defending-the-interface-by-owning-the-stack\\#the-tensions-ahead)\n\nMeta’s strategy is coherent, but not guaranteed. Several failure points remain:\n\n- **Model risk**: if open-weight Llama models lag frontier systems, Meta loses its commoditization leverage.\n- **Retention risk**: if assistants fail to create durable daily use, distribution advantages decay.\n- **Advertiser trust**: outcome pricing may stall if advertisers rebel against opaque attribution.\n- **Regulatory choke points**: data usage restrictions or compute regulation could erode Meta’s moat.\n- **Platform displacement**: Apple and Google could embed on-device agents that intercept intent before it touches Meta’s platforms.\n\nEach of these dynamics introduces uncertainty, but they also underscore why Meta is investing across the entire stack simultaneously. Defense of the tollbooth requires coverage on all flanks.\n\n## [Conclusion](https://darshshah.xyz/posts/meta-defending-the-interface-by-owning-the-stack\\#conclusion)\n\nMeta’s AI program is not a scattershot attempt to catch up with OpenAI or Anthropic. It is a deliberate defense of the most valuable real estate in the digital economy: the interface where intent originates.\n\n- **Compute independence** accelerates Meta’s learning and removes bottlenecks.\n- **Open-weight models** compress rivals’ margins and set industry defaults.\n- **Distribution at scale** ensures Meta’s assistant becomes the default entry point for billions.\n- **Outcome-based pricing** rewrites ad economics to lock in advertisers.\n\nAt the center of this stands Zuckerberg—unusually willing to bet big and act fast, even at trillion-dollar scale. The combination of vision and execution explains why Meta, despite its vast size, still operates with the urgency of a challenger.\n\nBut money and infrastructure are not destiny. Whether Meta can evolve into a genuine intellectual hub that attracts—not just buys—the world’s best AI talent will determine whether it remains the tollbooth of the digital age, or whether intent shifts permanently to someone else’s gate.",
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    "content": "There's a certain kind of intellectual flattery in claiming to think from first principles. It sounds rigorous. Elon Musk talks about it. It implies you're not just following the crowd, that you've done the hard work of questioning assumptions.\n\nBut I've noticed something about myself: I'm better at the deconstruction than the reconstruction.\n\nBreaking things down is satisfying. You find the atomic truths, the irreducible facts, and suddenly the conventional wisdom looks arbitrary. Why do we do it this way? Because everyone else does. The emperor has no clothes. You feel clever.\n\nThe harder part comes next. Once you've dismantled the existing structure, you have to build something new. And here's where most first-principles thinking quietly fails: the rebuild often looks suspiciously similar to what everyone else already concluded.\n\nThat's because the conventional wisdom usually exists for reasons. Not always good ones. But reasons. And those reasons exert a gravitational pull on even the most independent thinker. You end up at roughly the same destination, just with extra steps and the false confidence of someone who believes they arrived there independently.\n\nThe people who actually change things aren't just good at reasoning from fundamentals. They're willing to follow that reasoning somewhere uncomfortable. Somewhere that makes them look wrong until it makes them look prescient.\n\nFirst principles thinking is necessary but not sufficient. The sufficient part is conviction - the willingness to act on conclusions that seem obvious to you and absurd to everyone else. To hold a position long enough for reality to render its verdict.",
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    "content": "Wispr is an absolutely phenomenal product. But for my workflow, I couldn't justify paying for another monthly subscription, so I decided to make a local-only app instead.\n\nAt first, the project sounded straightforward: capture audio, run Whisper locally, and insert text wherever I was typing. Whisper existed. Local inference existed. Coding agents existed. The ingredients were all there.\n\nWhat I learned is that those ingredients were enough to make a prototype, not a product.\n\nThat is the core claim: the model created the capability, but the product emerged from the system around it.\n\nWhisper was necessary. It was not sufficient.\n\nWhat made Cadence usable had much less to do with \"pick the right model\" than I expected. The hard part was specifying the behavior around the model:\n\n- whether silence gets trimmed before inference\n- whether the first syllable gets clipped without a preroll buffer\n- whether live preview and final transcription should share a path\n- whether greedy decoding feels better than beam search for short dictation\n- whether the HUD clearly distinguishes recording from transcribing\n- whether keyboard shortcuts feel like gestures instead of configuration\n- whether macOS permissions feel product-like or sketchy\n\nThose are easy details to dismiss when you say \"build a local Whisper app.\" They are also the difference between something that technically works and something I actually want to use.\n\nOne of the biggest surprises was that many of the choices I thought of as inference settings were really UX decisions in disguise.\n\nTake greedy decoding versus beam search. In theory, that sounds like a model-quality question. In practice, for a dictation app, it is a latency question. The user does not experience \"decoding quality\" in the abstract. They experience the gap between releasing a shortcut and seeing text appear. That makes the setting part of the product, not just part of the model.\n\nThe same thing was true for silence trimming and preroll. If the app captures dead air, latency gets worse. If it starts listening too late, the first word gets clipped. Neither problem is solved by saying \"use a bigger model.\" They are pipeline problems, which means they are product problems.\n\nThat was the pattern across the whole build. The coding agent could implement changes quickly once I knew what was wrong. It could refactor the HUD, change defaults, wire up transcript history, and patch hotkey behavior. What it could not do reliably was notice why the experience felt off. I still had to diagnose the failure mode, decide what should be true, and rewrite the spec.\n\nThat is why I think the most defensible lesson from this project is broader than dictation:\n\nA model gives you capability. A product gives you reliable value.\n\nThe gap between those two things is where product work lives.\n\nThat is also why I think so many AI products break through later than the underlying model breakthrough suggests they should. The model makes a new behavior possible. The product only arrives once someone builds the workflow, defaults, feedback loops, state management, and trust scaffolding that let a normal person use that behavior without thinking about the machinery underneath.\n\nThat was true in my much smaller way with Cadence. Whisper made local dictation possible. Cadence only became usable once the orchestration around Whisper got good enough.\n\nI think the same pattern explains a lot of AI product adoption more generally. Transformers existed before ChatGPT mattered to normal users. Codex existed before coding tools became daily workflows for a much wider set of builders. The breakthrough is not just raw model capability. It is the surrounding system that makes the capability legible, reliable, and worth integrating into real work.\n\nThat is not a claim that models do not matter. They obviously do. A bad model limits the ceiling of the product.\n\nIt is a narrower claim: once the model clears a capability threshold, the bottleneck often shifts to orchestration, interface, and judgment.\n\nThat is what Cadence taught me.\n\nI did not build a useful dictation app by discovering a better model. I built a better product by learning that the model was only one component in a much larger system.\n\nAnd as code generation gets cheaper, I think that distinction matters more, not less.",
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    "content": "The line that kicked this off was something I came across on X from Henrique Dubugras (Brex):\n\n**\"Consensus is cosplay for avoiding accountability.\"**\n\nDramatic. Maybe too dramatic. But it stuck.\n\nConsensus, in teams, often feels like the polite way to move forward. Everyone gets heard, no one is shut down, decisions feel shared. But the more I thought about it, the more I noticed the shadow side: sometimes when everyone agrees, no one owns what happens next. A decision gets made, but there is no person attached to its success or failure. And without that closeness, who learns from how it actually plays out?\n\nAccountability, in my head, isn’t blame.\n\nIt’s not the label we apply when it breaks.\n\nIt sounds more like:\n\n> _I pushed for this._\n>\n> _I will follow where it goes._\n>\n> _I will adjust when reality pushes back._\n\nThe clearest example from my own work is from ShareChat.\n\nI pushed to move a huge ad sales team onto Salesforce because I wanted better revenue visibility and a way for Strategic Finance to support the business with cleaner forecasting. It was probably early for where the company was. It was definitely expensive in both money and time.\n\nBut I owned it.\n\nI handled the migration pain.\n\nI worked with the sales org on adoption.\n\nI watched the dashboards and the misses and the friction.\n\nWhen something broke, it was my problem to fix.\n\nAnd because I could not walk away from it, I learned fast.\n\nSome parts worked beautifully.\n\nSome absolutely did not.\n\nBoth taught me something real.\n\nAnd that is where the Henrique quote keeps echoing.\n\nWhen ownership falls to someone purely because of hierarchy and not belief, the intensity drops. The feedback loop softens. Learning becomes second-hand instead of lived.\n\nMaybe nothing collapses. Plenty of companies run fine like that. But the work loses a bit of sharpness. A bit of skin in the game.\n\nThe thought I keep circling:\n\n> **Accountability only matters if it creates learning.**\n>\n> Otherwise it’s just names on a slide.",
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    "content": "There’s a friend who’s been gently (and persistently) nudging me to do it — to stop overthinking, start small, and just put thoughts into words. I always nodded along, agreed in principle… and then let life get in the way.\n\nBusiness school, work, other shiny distractions — the usual suspects. Writing always felt like something I _should_ do, something I _wanted_ to do, but never something I gave time to.\n\nLately, though, I’ve been reading a lot. Mostly in AI, sometimes tech more broadly. And it’s exciting — the field moves fast, but even more interesting are the ideas under the surface. The patterns, the shifts, the tensions. The stuff you don’t always see in headlines, but feel when you're paying attention.\n\nWhat I’ve realized is: I end up carrying around a lot of _unformed_ thoughts. Half-ideas. Instincts. Hunches that haven’t found structure yet. Some of them, I think, might actually be kind of profound — _if_ I give them space. Not polish, not a perfect argument, just... a space.\n\nThat’s what _Uncompiled_ is.\n\nA place for those ideas before they’re ready. Before they’ve been optimized or cleaned up. The name is literal — these posts aren’t fully “built,” and maybe they won’t ever be. But writing them down lets me learn in public. It lets others connect threads I didn’t see. And it leaves behind a trail of what I was thinking, when.\n\nMaybe someday I’ll come back and compile it all. Or maybe not.\n\nFor now, this is just a scratchpad with a publish button. If you’re reading this: thanks for being here.",
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    "content": "There's a question I keep coming back to that I don't think the industry has a clean answer to yet: in a world where agents are doing the work, how do you price the software they work through?\n\nLet me try to think through it.\n\nMost enterprise software we've built over the last few decades has been a system of record. Salesforce holds your customer relationships. Snowflake holds your data. Excel holds your models. These weren't just storage — they were the primary interface between your business and its information, and the humans navigating them were the unit of value. So pricing by seat made sense. You scaled headcount, you scaled seats, the model worked.\n\nAgents break that assumption. Not because systems of record go away — enterprises still want secure, governed, structured environments, and I think that preference is actually going to intensify as agents get write access to sensitive systems. But the human seat stops being the natural unit of value when an agent can navigate these systems directly. The system of record gets demoted. Not eliminated — demoted. From engine to plumbing.\n\nHere's where I want to be careful though, because plumbing isn't automatically low-margin. AWS is plumbing and it's one of the most profitable businesses ever built. The risk for incumbent SaaS isn't just that they become infrastructure — it's that they become _interchangeable_ infrastructure. The margin compression comes from substitutability. If an agent can be pointed at Salesforce or HubSpot or a custom CRM with roughly equal ease, the switching costs that justified premium pricing start to erode. That's the real threat, and it's worth naming precisely.\n\nSo who actually forces the repricing? I don't think it's new entrants alone, and I don't think Salesforce or Snowflake voluntarily walk away from seat-based models. The forcing function is the enterprise buyer. Think about what happens when a CFO looks at a 500-seat Salesforce contract and realizes their agents don't need seats — they need API access. That's a procurement conversation that's already starting to happen in some companies. Buyers will drive this repricing faster than competitive pressure does, because buyers have contracts up for renewal and real leverage.\n\nThat raises the harder question: what does the alternative actually look like? My intuition is work-based or workflow-based pricing — you pay for what gets done, not for who's doing it. For the agent layer specifically, token-based pricing with some markup is probably the natural model. But that's a different problem than how systems of record get repriced. If an agent touches Salesforce 10,000 times a day, does Salesforce charge per API call? Per workflow completed? Per business outcome? There's no settled answer here, and I think that ambiguity is where a lot of the strategic uncertainty lives right now.\n\nThe full-stack question sits underneath all of this. Most companies today operate cleanly in one world — they're either building the system of record or building the agent layer. Very few are doing both, and the ones trying find it genuinely hard. Part of that is product architecture — agents need read and write access to systems that were designed around human interaction patterns. Part of it is trust — giving an agent write access to your CRM or your financial data is a different risk profile than giving a human a seat. And part of it is go-to-market: how do you sell a product that serves both a human workflow and an automated one without cannibalizing yourself?\n\nThe companies that figure out that last question — not just the product, not just the architecture, but the _pricing design_ — are probably the ones that define what enterprise software looks like in five years. Right now, most of the conversation is about what agents can do. I think the more durable question is how we charge for it.\n\nI don't have a clean answer. But I think it's the right thing to be sitting with.",
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    "content": "# Post by @trq212\n\n> One of my fav hacks has been to give skills a “last used” date, and have my Claudemd file audit all unused skills every 2 weeks. Keeps the skills list fresh and prevents it from bloating or overlapping skills!\n\nLikes: 159 | Retweets: 3\n\n### 2. Thread Post\nAuthor: @trq212 Posted: Tue, 17 Mar 2026 17:06:34 GMT URL: [https://x\\.com/trq212/status/2033953147765592194](https://x.com/trq212/status/2033953147765592194)\n\n> ohh thats really smart actually, do you do it programatically with a hook?\n\nLikes: 43 | Retweets: 0\n\n### 3. Thread Post\nAuthor: @heyNaitik Posted: Tue, 17 Mar 2026 17:24:59 GMT URL: [https://x\\.com/heyNaitik/status/2033957782039498842](https://x.com/heyNaitik/status/2033957782039498842)\n\n> nope, just instructions in my Claudemd file so it fires every time I load a CC instance and scans to see if \"last audit date\" &gt;= 2 weeks\n\nLikes: 11 | Retweets: 0\n\n### 4. Thread Post\nAuthor: @trq212 Posted: Tue, 17 Mar 2026 18:06:22 GMT URL: [https://x\\.com/trq212/status/2033968197809148120](https://x.com/trq212/status/2033968197809148120)\n\n> ah, a hook might be better!\n\nLikes: 19 | Retweets: 0\n\n### 5. Thread Post\nAuthor: @heyNaitik Posted: Tue, 17 Mar 2026 18:07:00 GMT URL: [https://x\\.com/heyNaitik/status/2033968360053215275](https://x.com/heyNaitik/status/2033968360053215275)\n\n> true! setting it up now :D\n\nLikes: 7 | Retweets: 0\n\n## Top Comments\n\n### 1. @Cascadia\\_AI\nAuthor: Jared Dunn Posted: Tue, 17 Mar 2026 17:29:37 GMT URL: [https://x\\.com/Cascadia\\_AI/status/2033958949620486398](https://x.com/Cascadia_AI/status/2033958949620486398)\n\n> Ever since I got Claude. I spend more time reading things like this and not actually working on projects. I'm not all that bright and it makes my head hurt. Can you guys start making these into cartoons?\n\nLikes: 114\n\n### 2. @luongnv89\nAuthor: Luong NGUYEN Posted: Tue, 17 Mar 2026 17:57:21 GMT URL: [https://x\\.com/luongnv89/status/2033965927813833065](https://x.com/luongnv89/status/2033965927813833065)\n\n> great write up, thanks.\n>\n> I feel there are still some missing/ or at least some problems we should address sooner or later:\n>\n> - versioning: when you and your college use the same skills in the same repo - but got different results\n>\n> - role/expert domain: idk how to call it out, but imagine you got a skill to analyze a data file, 2 different roles will likely want to see different point of view of the data.\n>\n> - owner/category: soon we will have many duplicated skills. design skill from Anthropic, design skill from google, etc. model will be confused, and you still want to keep them all in your skill set.\n>\n> - evaluation: this is kind of tricky, how can we say design skill from google is better the design skill from Anthropic.\n>\n> - a proper agent skill manager: for audit, install, uninstall, versioning, etc. I am working on my own version (cli &amp; tui:\n\nLikes: 76\n\n### 3. @LostSynthCat\nAuthor: LostSynthCat Posted: Tue, 17 Mar 2026 17:30:51 GMT URL: [https://x\\.com/LostSynthCat/status/2033959261253111970](https://x.com/LostSynthCat/status/2033959261253111970)\n\n> Can you please write blog posts instead of X articles\n\nLikes: 68\n\n### 4. @chrislakin\nAuthor: Chris Lakin Posted: Tue, 17 Mar 2026 17:21:56 GMT URL: [https://x\\.com/chrislakin/status/2033957015488483388](https://x.com/chrislakin/status/2033957015488483388)\n\n> How do you write these articles? Surely Claude helps, but also they never seem _that_ AI generated…?\n\nLikes: 61\n\n### 5. @zarazhangrui\nAuthor: Zara Zhang Posted: Tue, 17 Mar 2026 18:19:31 GMT URL: [https://x\\.com/zarazhangrui/status/2033971508201328758](https://x.com/zarazhangrui/status/2033971508201328758)\n\n> Thanks for the writeup! \n> I noticed that the distribution &amp; measurement of skill are all tailored towards internal use cases; what about public-facing/open source skills? I have built some skills but current there's no marketplace to distribute/monetize them, and no way for me to know how many people have used it",
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    "content": "# Post by @trq212\n\n> Last week, we released dynamic workflows in Claude Code. Claude can now write its own harness on the fly, custom-built for the task at hand.\n>\n> While the default Claude Code harness is built for coding, it is also useful for many other types of tasks because, as it turns out, many tasks resemble coding tasks. But there are certain classes of tasks where we have had to build custom harnesses on top of Claude Code to achieve peak performance such as Research, security analysis, agent teams, or Code Review.\n>\n> Workflows allow you to dynamically create harnesses that enable Claude to solve all of those problems and more natively inside of Claude Code. You can also share and re-use these workflows with others.\n>\n> In this article, I’ll cover my initial workflows experiences and learnings so you can take full advantage. \n> That said, best practices are still developing! Dynamic workflows often use more tokens, so think carefully about when and how to use them.\n>\n> Note: this post is also available on the Claude Blog\n>\n> Example prompts\n> Before diving into the technical details, I’d like to start with some example prompts to get you thinking about the possibilities with workflows:\n> \"This test fails maybe 1 in 50 runs. Set up a workflow to reproduce it, form theories and adversarially test them in worktrees /goal don't stop until one theory works.\"\n> \"Using a workflow, go through my last 50 sessions and mine them for corrections I keep making and turn the recurring ones into CLAUDE.md rules\"\n> “Use a workflow to dig through #incidents in Slack for the past six months and find recurring root causes where nobody has filed a ticket.\"\n> \"Take my business plan and run a workflow where different agents tear it apart from an investor's, a customer's, and a competitor's perspective.\"\n> \"Here's a folder of 80 resumes, use a workflow to rank them for the backend role and double-check the top ten. Interview me using the AskUserQuestion tool for a rubric.\"\n> \"I need a name for this CLI tool. Use a workflow to brainstorm a bunch of options and run a tournament to pick the top 3.\"\n> \"Use a workflow to rename our User model to Account everywhere.\"\n> “Go through my blog post draft and using a workflow verify every technical claim against the codebase, I don't want to ship anything wrong.\"\n>\n> How dynamic workflows work\n> Dynamic workflows execute a javascript file with a few special functions that help spawn and coordinate subagents:\n>\n> Dynamic workflows also include standard JavaScript functions like JSON, Math, and Array, to help process data.\n> It’s particularly useful to know that dynamic workflows can decide which models an agent uses and whether subagents are run in their own worktree, allowing Claude to choose the intelligence level and isolation needed.\n> If a workflow is interrupted, for example by user action or quitting the terminal, resuming the session will allow the workflow to pick up where it left off.\n>\n> Why dynamic workflows\n> When you ask the default Claude Code harness to do a task, it needs to both plan and execute in the same context window. For many coding tasks, this is highly effective, but it can sometimes break down over long-running, massively parallel and/or highly structured adversarial tasks.\n>\n> This is because the longer Claude works on a complex task in a single context window, the more it becomes susceptible to a few specific failure modes:\n>\n> Agentic laziness refers to when Claude stops before finishing a particularly complex, multi-part task and declares the job done after partial progress, for example addressing 20 of the 50 items in a security review.\n> Self-preferential bias refers to Claude’s tendency to prefer its own results or findings, especially when asked to verify or judge them against a rubric.\n> Goal drift refers to the gradual loss of fidelity to the original objective across many turns, especially after compaction. Each summarization step is lossy, and details like edge-case requirements or \"don't do X\" constraints can get lost.\n>\n> Creating a workflow helps combat these by orchestrating separate Claudes with their own context windows and focused, isolated goals.\n>\n> Dynamic vs static workflows\n> You may have previously created a static workflow using the Claude Agent SDK or claude -p to coordinate multiple instances of Claude Code together.\n> But because static workflows need to work for all edge cases, they are usually more generic. With Claude Opus 4.8 and dynamic workflows, Claude is now intelligent enough to write a custom harness tailor-made for your use case.\n>\n> Helpful patterns when using dynamic workflows\n> You can start using dynamic workflows just by asking Claude to make one, or by using the trigger word “ultracode” to ensure that Claude Code creates a workflow.\n>\n> But building a mental model for how dynamic workflows work will help you understand when to use them and how you might nudge Claude via prompts.\n>\n> There are a few common patterns that Claude might use and compose together when building workflows:\n>\n> Classify-and-act\n> Use a classifier agent to decide on the type of task, and then route to different agents or behavior based on the task. Or, use a classifier at the end to determine output.\n>\n> Fan-out-and-synthesize\n> Split up a task into many smaller steps, run an agent on each step and then synthesize those results. This is particularly useful for when there are a large number of smaller steps, or when each step benefits from its own clean context window so they don't interfere or cross-contaminate. The synthesize step is a barrier—it waits for all the fan-out agents, then merges their structured outputs into one result.\n>\n> Adversarial verification\n> For each spawned agent, run a separate spawned agent to adversarially verify its output against a rubric or criteria.\n>\n> Generate-and-filter\n> Generate a number of ideas on a topic and then filter them by a rubric or by verification, dedupe duplicates and return only the highest quality, tested ideas.\n>\n> Tournament\n> Instead of dividing the work, have agents compete on it. Spawn N agents that each attempt the same task using different approaches. Prompts or models then judge the results in a pairwise fashion using a judging agent until you have a winner.\n>\n> Loop until done\n> For tasks with an unknown amount of work, loop spawning agents until a stop condition is met (no new findings, or no more errors in the logs) instead of a fixed number of passes.\n>\n> Use cases\n> Think creatively of when and how to ask Claude Code to make dynamic workflows. I’ve found that workflows are sometimes even more useful for non-technical work.\n>\n> Migrations and refactors\n> Bun was rewritten from Zig to Rust using workflows. You can read more about how that was done in Jarred’s X thread.\n> The key is to break down the task into a series of steps that need to be operated on for example callsites, failing tests, modules, etc. Spin off a subagent for every fix in a worktree to make the fix, then have another agent adversarially review, and merge them. Consider telling the agent not to use resource intensive commands so that you can maximally parallelize without running out of resources on your machine.\n>\n> Deep research\n> We published a deep research skill (/deep-research) inside Claude Code that uses dynamic workflows. Specifically, it fans-out web searches, fetches sources, adversarially verifies their claims, and synthesizes a cited report.\n> But you may do this sort of research for more than just web searches. For example, asking Claude to compile a status report from context in Slack or to research how a feature works by exploring a codebase in-depth.\n>\n> Deep verification\n>\n> On the other hand, if you have a report where you want to check and source every factual claim that it references you may want to generate a workflow which has one agent identify all of the factual claims and then spin off a subagent to check each one in-detail. You could also have a verification agent check the source subagent to make sure its source is high quality.\n>\n> Sorting\n>\n> You may have a list of items that you want to sort by some qualitative measurement that you believe that Claude Code is good at evaluating, for example: support tickets sorted by severity of the bug. But if you try to sort 1000+ rows in one prompt, quality degrades and it won't fit in context. Instead run a tournament, a pipeline of pairwise-comparison agents (comparative judgment is more reliable than absolute scoring), or bucket-rank in parallel then merge. Each comparison is its own agent, so the deterministic loop holds the bracket and only the running order stays in context.\n>\n> Memory and rule adherence\n>\n> If you have a particular set of rules that you find Claude misses or struggles with, even when put into the CLAUDE.mds, create a workflow with a list of rules that must be checked by verifier agents—one verifier per rule. Creating a skeptic persona subagent to review the rules to make sure they are in line will help avoid too many false positives.\n> The reverse direction works too: mine your recent sessions and code review comments for corrections you keep making, cluster them with parallel agents, adversarially verify each candidate (would this rule have prevented a real mistake?), and then distill the survivors back into a CLAUDE.md.\n>\n> Root-cause investigation\n> Debugging works best when you come up with several independent hypotheses and test them, but if you’re only using one context window, Claude can run into self-preferential bias.\n>\n> A workflow can structurally prevent this by spinning up agents to generate hypotheses from disjoint evidence. For example, separate agents for logs, files, and data. Each hypothesis can then face a panel of verifiers and refuters.\n> This isn't just for code. Workflows can be used for sales (why did sales drop in March?), data engineering (why did this pipeline fail?), or any post-mortem exercise.\n>\n> Triaging at scale\n>\n> Every team has a support queue, bug reports, or some other backlog that cannot be fully processed by humans.\n> A triage workflow classifies each item, dedupes against what's already tracked, and takes action. This could mean attempting the fix or escalating to a human user.\n> A useful pattern for triage workflows is quarantine. This involves barring the agents that read untrusted public content from taking high-privilege actions, which are instead done by the agents in charge of acting on the information.\n> Pair triage workflows with /loop to have Claude do this continuously.\n>\n> Exploration and taste\n> Workflows can be useful when exploring different approaches to a solution, especially when it is taste based, like design or naming, and would benefit from a rubric.\n> Try asking Claude to explore a bunch of solutions, and give a review agent  a rubric for what a good solution looks like. The task is complete when the review agent feels like it has met the criteria. Solutions can also be ordered or selected via a tournament based on the rubric.\n>\n> Evals\n> You can run lightweight evals for particular tasks by spinning off separate agents in a worktree and then spinning off comparison agents to compare and grade the specific outputs against a rubric. For example, evaluating and then refining a skill you’ve created against a particular criteria.\n>\n> Model and intelligence routing\n> Create a classifier agent tuned to your tasks that decides which model to use. This can be helpful when your task will involve many tool calls and conducting research prior to execution can identify the best model for the job.\n> For example, the best model for the task “explain how the auth module works” depends on how many files in the auth module there are and the shape of the codebase. A classifier agent can do this research and then route to Sonnet or Opus based on the expected complexity of the task.\n>\n> When not to use dynamic workflows\n> Workflows are new. While there are many use cases where it will create outsized results, they are not needed for every task and may end up using significantly more tokens.\n>\n> It’s best to use workflows creatively to push Claude Code in ways that you haven’t previously. For regular coding tasks, try and ask yourself does it really need more compute? For example, most traditional coding tasks do not need a panel of 5 reviewers.\n>\n> Tips for building dynamic workflows\n> Prompting\n> Detailed prompting, using the specific techniques we described above, for dynamic workflows creates the best results.\n>\n> Workflows are not just for large tasks. You can prompt the model to use a “quick workflow.” For example, you can create a quick adversarial review of an assumption.\n>\n> Combine with /goal and /loop\n> When using workflows that can be repeated, for example triage, research, or verification, pair them with /loop to be run at regular intervals, and /goal to set a hard completion requirement.\n>\n> Token usage budgets\n> You can set explicit token usage budgets for dynamic workflows to limit how many tokens a task uses. You can prompt it with a budget like: “use 10k tokens,” which will set the cap.\n>\n> Saving and sharing dynamic workflows\n> You can save workflows by pressing “s” in the workflow menu. You can check these into ~/.claude/workflows or distribute them via a skill.\n>\n> To share them via a skill, put your JavaScript workflow files in the skill and folder and reference them in the SKILL.MD. To allow for more flexibility, you may want to prompt Claude to think of the workflows in the skill as a template instead of a script that needs to be run verbatim.\n>\n> A whole new world\n> Workflows are a helpful new way to extend Claude Code. I encourage you to think of this as a starting point, there's still much to discover in how to use them best. Let us know what you find.\n>\n> Thariq Shihipar and Sid Bidasaria (@sidbid) are members of technical staff at Anthropic, working on Claude Code.\n\n## Top Comments\n\n### 1. @badlogicgames\nAuthor: Mario Zechner Posted: Tue, 02 Jun 2026 21:58:27 GMT URL: [https://x\\.com/badlogicgames/status/2061930469566943327](https://x.com/badlogicgames/status/2061930469566943327)\n\n> good stuff, i'd like to understand what caveats this has:\n>\n> &gt; resuming the session will allow the workflow to pick up where it left off.\n>\n> it seems hard to ensure general durability for arbitrary workflows?\n\nLikes: 57\n\n### 2. @ryanvogel\nAuthor: vogel Posted: Tue, 02 Jun 2026 20:29:40 GMT URL: [https://x\\.com/ryanvogel/status/2061908125247050028](https://x.com/ryanvogel/status/2061908125247050028)\n\n> (No text content)\n\nLikes: 52\n\n### 3. @tmuskal\nAuthor: Tal Muskal Posted: Thu, 04 Jun 2026 17:34:59 GMT URL: [https://x\\.com/tmuskal/status/2062588940545040617](https://x.com/tmuskal/status/2062588940545040617)\n\n> dynamic workflows for codex/pi/copilot/cursor/hermes/claude-code/gemini, available today (and for months). orchestrate non-claude + local LLMs as subtasks, and even non-agentic atoms like npm test for real determinism. babysitter processes: https://github.com/a5c-ai/babysitter\n\nLikes: 42\n\n### 4. @bbishdotdev\nAuthor: Brenden Bishop Posted: Tue, 02 Jun 2026 21:49:24 GMT URL: [https://x\\.com/bbishdotdev/status/2061928191078408617](https://x.com/bbishdotdev/status/2061928191078408617)\n\n> “…workflows tend to use more tokens so use wisely!”\n>\n> Also Thariq: \n>\n> “I need a name for this CLI tool. Use a workflow to brainstorm a bunch of options and run a tournament to pick the top 3.”\n\nLikes: 40\n\n### 5. @MindBranches\nAuthor: MindBranches Posted: Wed, 03 Jun 2026 01:51:01 GMT URL: [https://x\\.com/MindBranches/status/2061988999162368027](https://x.com/MindBranches/status/2061988999162368027)\n\n> A harness for every task: dynamic workflows in Claude Code\n>\n> article summary in 4k light and dark",
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    "content": "# Post by @trq212\n\n> This is now also on the Claude Blog.\n>\n> Markdown has become the dominant file format used by agents to communicate with us. It’s simple, portable, has some rich text capability and is easy for you to edit. Claude has even gotten surprisingly good at using ASCII to make diagrams inside of markdown files.\n> But as agents have become more and more powerful, I have felt that markdown has become a restricting format. I find it difficult to read a markdown file of more than a hundred lines. I want richer visualizations, color and diagrams and I want to be able to share them easily.\n> I'm also increasingly not editing these files myself, but using them as specs, reference files, brainstorming outputs, etc. When I do make edits, I’m usually prompting Claude to edit them, which removes one of markdown’s largest benefits.\n> I’ve started preferring HTML as an output format instead of Markdown and increasingly see this being used by others on the Claude Code team, this is why.\n> (if you want to start with some examples, you can see a bunch here: https://thariqs.github.io/html-effectiveness, just be sure to come back and read more about why)\n>\n> Why HTML?\n> Information Density\n>\n> HTML can convey much richer information compared to markdown. It can of course do simple document structure like headers and formatting, but it can also represent all sorts of other information such as:\n> Tabular data using tables\n> Design data with CSS\n> Illustrations with SVG\n> Code snippets with script tags\n> Interactions using HTML elements with javascript + CSS\n> Workflows using SVG and HTML\n> Spatial data using absolute positions and canvases\n> Images using image tags\n> I would go so far as to say that there is almost no set of information that Claude can read that you cannot fairly efficiently represent with HTML. This makes it a highly efficient way for the model to communicate in-depth information to you and for you to review it.\n> I’ve found that in the absence of being able to do this, the model may do more inefficient things in markdown like ASCII diagrams or, my favorite, estimating colors with unicode characters like in this screenshot from Claude Code.\n>\n> Visual Clarity &amp; Ease of Reading\n>\n> As Claude is able to do more complex work, it is also writing larger and larger specs and plans. In practice, I've found I tend to not actually read more than a 100-line markdown file, and I certainly am not able to get anyone else in my organization to read it.\n> But HTML documents are much easier to read, Claude can organize the structure visually to be ideal to navigate with tabs, illustrations, links, etc. It can even be mobile responsive so you can read it differently based on your form factor.\n> Ease of Sharing\n> Markdown files are fairly hard to share since most browsers do not render them natively well. You often have to add them as attachments to emails or messages.\n> With HTML, as long as you upload the file (for example to S3), you can share the link easily. Your colleagues can open it wherever you wish and easily reference it.\n> The chance of someone actually reading your spec, report or PR writeup is much much higher if it’s in HTML.\n> Two-way Interaction\n>\n> HTML can allow you to interact with the document, for example you might want to ask it to add sliders or knobs to adjust a design or allow you to tweak different options in the algorithm to see what happens. You can also ask it to let you copy these changes into a prompt to paste back into Claude Code. \n>\n> Read more about my playgrounds post to see examples of this two way interaction: https://x.com/trq212/status/2017024445244924382\n> Data Ingestion\n> Why use Claude Code to make HTML files instead of ClaudeAI or Claude Design for example? One of the biggest reasons is all the context Claude Code can ingest. \n>\n> For example, when writing this article, I asked Claude Code to read through my code folder and find all the HTML files I’ve generated, group and categorize them and then make an HTML file with all diagrams representing each type. The diagrams you see in this article are a direct result of that.\n> Besides the file system, Claude Code can find additional context using your MCPs (like Slack, Linear, etc.), your web browser (with Claude in Chrome), your git history, etc.\n> It’s Joyful\n> Making HTML documents with Claude is just more fun and makes me feel more involved and invested in the creation, and that by itself is enough.\n> How to Get Started\n> I’m a little bit afraid that people will read this article and turn it into a /html skill or something. While there might be some value in that, I want to emphasize that you don’t need to do much to get Claude to do this. You can just ask it to “make a HTML file” or “make a HTML artifact”.\n> The trick is knowing what you want the artifact to do and how you might use it. You may over time make a skill, but for now I’d suggest just prompting from scratch to get a hang of how to use it in different cases.\n> Use Cases\n> To make this more concrete, I’ve made many different HTML files for different use cases. You can view all of them here: https://thariqs.github.io/html-effectiveness/ but here’s an overview.\n>\n> Specs, Planning &amp; Exploration\n> HTML is a rich canvas for Claude to dive into a problem. When I start working on a problem instead of a simple markdown plan I expect to make a web of HTML files. For example, I might start with asking Claude Code to brainstorm and create some explorations of different options. I would then ask it to expand more into one, maybe make mockups or code snippets. Finally, when I feel good I’ll ask it to write an implementation plan. When I’m happy with the plan I’ll create a new session and pass in all of these files for it to implement.\n> When verifying I’ll also ask the verification agent to read in the files and it will have much broader context on what is needed.\n>\n> Example Prompts:\n> I'm not sure what direction to take the onboarding screen. Generate 6 distinctly different approaches — vary layout, tone, and density — and lay them out as a single HTML file in a grid so I can compare them side by side. Label each with the tradeoff it's making.\n> Create a thorough implementation plan in a HTML file, be sure to make some mockups, show data flow and add important code snippets I might want to review. Make it easy to read and digest.\n>\n> Use Cases:\n> Exploring other ways to implement something in code\n> Exploring multiple visual designs\n> Code Review &amp; Understanding\n> Code can be difficult to read in a Markdown file.  But with HTML we can render diffs, annotations, flowcharts, modules, etc.  Use this to understand code that the agent has written, to get code review or to explain a PR to someone reviewing your code. I find this often works better than the default Github diff view, and I attach a HTML code explainer to every PR I make now. \n>\n> Example prompt:\n> Help me review this PR by creating an HTML artifact that describes it. I'm not very familiar with the streaming/backpressure logic so focus on that. Render the actual diff with inline margin annotations, color-code findings by severity and whatever else might be needed to convey the concept well.\n>\n> Use Cases:\n> Creating a PR\n> Reviewing a PR\n> Understanding a topic in Code\n> Design &amp; Prototypes\n> Claude Design is based on HTML because HTML is incredibly expressive at design, even if your end surface is not HTML. Claude can sketch out a design in HTML and then write it in your language of choice, be it React, Swift, etc.\n> You can also prototype interactions, such as animations, actions, etc. Consider asking Claude to make sliders, knobs, etc. to tune in exactly what you’re looking for.\n>\n> Example prompt:\n> I want to prototype a new checkout button, when clicked it does a play animation and then turns purple quickly. Create a HTML file with several sliders and options for me to try different options on this animation, give me a copy button to copy the parameters that worked well.\n>\n> Use this for:\n> Creating design system artifacts\n> Adjusting components\n> Visualizing component libraries\n> Prototyping Joyful Animations\n> Reports, Research &amp; Learning\n> Claude Code is incredibly good at synthesizing information across multiple data sources and converting it into a report for readability. You can prompt Claude to search your Slack, your codebase, git history, the internet, etc. and use it to generate extremely readable reports for yourself, for leadership, for your team, etc. \n> You could assemble this in the form of a long HTML document, an interactive explainer or even a slideshow/deck. Ask Claude to use SVG for diagrams to help visualize it.\n>\n> For example, for my posts on prompt caching, I asked Claude to prepare an in-depth research file in HTML for me to read on all of our changes to prompt caching after reading the git history.\n>\n> Example prompt:\n> I don't understand how our rate limiter actually works. Read the relevant code and produce a single HTML explainer page: a diagram of the token-bucket flow, the 3–4 key code snippets annotated, and a \"gotchas\" section at the bottom. Optimize it for someone reading it once.\n>\n> Use this for:\n> Summarize how a feature works\n> Explain a concept to me\n> Weekly status reports to your boss\n> Incident reports to your leadership\n> SVG illustrations, flowcharts, technical diagrams, etc\n> Custom Editing Interfaces\n> Sometimes it’s hard to describe what you want purely in a text box. In this case, I'll ask Claude to build me a throwaway editor for the exact thing I'm working on. Not a product, or a reusable tool, but a single HTML file, purpose-built for this one piece of data.\n> The trick is always to end with an export: a \"copy as JSON\" or \"copy as prompt\" button that turns whatever I did in the UI back into something I can paste into Claude Code.\n>\n> Example prompts:\n> I need to reprioritize these 30 Linear tickets. Make me an HTML file with each ticket as a draggable card across Now / Next / Later / Cut columns. Pre-sort them by your best guess. Add a \"copy as markdown\" button that exports the final ordering with a one-line rationale per bucket.\n> Here's our feature flag config. Build a form-based editor for it,  group flags by area, show dependencies between them, warn me if I enable a flag whose prerequisite is off. Add a \"copy diff\" button that gives me just the changed keys.\n> I'm tuning this system prompt. Make a side-by-side editor: editable prompt on the left with the variable slots highlighted, three sample inputs on the right that re-render the filled template live. Add a character/token counter and a copy button.\n>\n> Use this for:\n> Reordering, triaging, or bucketing anything (tickets, test cases, feedback)\n> Editing structured config (feature flags, env vars, JSON/YAML with constraints)\n> Tuning prompts, templates, or copy with live preview\n> Curating datasets, approve/reject rows, tag examples, export the selection\n> Annotating a document, transcript, or diff and exporting the annotations\n> Picking values that are painful to express in text: colors, easing curves, crop regions, cron schedules, regexes.\n> Frequently Asked Questions\n> I’ve been telling many people about how I’ve switched to HTML and I’ve seen a few repeated questions.\n> Isn’t it less token efficient?\n> While markdown often uses fewer tokens, I’ve found that the added expressiveness of HTML and the much higher likelihood of me reading it means I get overall better output. With the 1MM context window in Opus 4.7, the increased token usage is not really noticeable in the context window.\n> When do you use markdown for now?\n> I have honestly stopped using markdown altogether for almost everything, but I’m probably far on the HTML maximalist side of things.\n> How do I view the HTML file? \n> I tend just open it in a browser locally (you can ask Claude to open it), or upload to S3 if you want a shareable link.\n> Doesn't this take longer to generate than markdown? \n> This does take longer! HTML can take 2-4x longer than Markdown, but I've found the results are worth it.\n> What about version control? \n> This is honestly one of the biggest downsides of HTML, HTML diffs are noisy and hard to review compared to Markdown.\n> How do I get Claude to match my taste / not make it ugly? \n> The frontend design plugin helps Claude make good HTML files. But to match your own companies style, you can create a single design system HTML file by pointing Claude at your codebase. You can then use that design system file as a reference for other html files.\n> Stay in the Loop\n> All of the above is to say that I think the real reason I use HTML is that I feel much more in the loop with Claude. I had begun to fear that because I had stopped reading plans in depth I would simply have to leave Claude to make its choices.\n> But I am happy to say instead that I feel more in the loop than ever before when using HTML. I hope you do too.\n\n## Thread\n\n### 1. Thread Post\nAuthor: @trq212 Posted: Tue, 19 May 2026 21:03:18 GMT URL: [https://x\\.com/trq212/status/2056843158965858380](https://x.com/trq212/status/2056843158965858380)\n\n> Now also on the Claude Blog: https://claude.com/blog/using-claude-code-the-unreasonable-effectiveness-of-html\n\nLikes: 69 | Retweets: 6\n\n## Top Comments\n\n### 1. @mcvrn\nAuthor: Can Vuran Posted: Fri, 08 May 2026 18:52:32 GMT URL: [https://x\\.com/mcvrn/status/2052823985327317115](https://x.com/mcvrn/status/2052823985327317115)\n\n> “I’m don’t like reading long markdown files.”\n>\n> Writes a loong markdown x post.\n\nLikes: 652\n\n### 2. @somyatwts\nAuthor: Somya Posted: Fri, 08 May 2026 18:08:27 GMT URL: [https://x\\.com/somyatwts/status/2052812892869951567](https://x.com/somyatwts/status/2052812892869951567)\n\n> partially agree. html is great for prototypes, mockups, testing, and previews\n>\n> but Markdown is superior 🐐 for text-focused content - code, plans, documentation.\n> html implementation varies; md is standard.\n>\n> imo use html when you have more illustrations than text.\n\nLikes: 118\n\n### 3. @ItsOriMane\nAuthor: Ori Mannheim Posted: Fri, 08 May 2026 19:08:25 GMT URL: [https://x\\.com/ItsOriMane/status/2052827982524297356](https://x.com/ItsOriMane/status/2052827982524297356)\n\n> When you just learned how to use markdown file and now they want you to switch to HTML\n\nLikes: 92\n\n### 4. @mher\\_ai\nAuthor: Mher Posted: Fri, 08 May 2026 19:09:28 GMT URL: [https://x\\.com/mher\\_ai/status/2052828248099406240](https://x.com/mher_ai/status/2052828248099406240)\n\n> For those of us who use Claude Code in the terminal, how do you suggest we leverage HTML? Just had it produce files and open in browser?\n\nLikes: 41\n\n### 5. @petergyang\nAuthor: Peter Yang Posted: Fri, 08 May 2026 18:04:04 GMT URL: [https://x\\.com/petergyang/status/2052811789025567112](https://x.com/petergyang/status/2052811789025567112)\n\n> Banger - what about using html to make product videos - any useful skills or prompts for that?",
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    "content": "# Post by @trq212\n\n> In my recent calls with Claude Code users, one theme keeps coming up: the 1M token context window is a double-edged sword. \n> It lets Claude Code operate autonomously for longer and handle tasks more reliably, but it also opens the door to context pollution if you're not deliberate about managing your sessions.\n> Session management matters more than ever and there seem to be a lot of questions about it. Do you keep one session open in a terminal, or two? Start fresh with every prompt? When should you use compact, rewind, or subagents? What causes a bad compact?\n> There’s a surprising amount of detail here that can really shape your experience with Claude Code and almost all of it comes from managing your context window.\n> A Quick Primer on Context, Compaction &amp; Context Rot\n>\n> The context window is everything the model can \"see\" at once when generating its next response. It includes your system prompt, the conversation so far, every tool call and its output, and every file that's been read. Claude Code has a context window of one million tokens.\n> Unfortunately using context has a slight cost, which is often called context rot. Context rot is the observation that model performance degrades as context grows because attention gets spread across more tokens, and older, irrelevant content starts to distract from the current task. For our 1MM context model, we see some level of context rot happen around ~300-400k tokens, but it is highly dependent on the task- not a fast rule.\n> Context windows are a hard cutoff, so when you’re nearing the end of the context window, you will need to summarize the task you’ve been working on into a smaller description and continue the work in a new context window, we call this compaction. You can also trigger compaction yourself.\n>\n> Every Turn Is a Branching Point\n> Say you've just asked Claude to do something and it's finished, you’ve now got some information in your context (tool calls, tool outputs, your instructions) and you have a surprising number of options for what to do next:\n> Continue — send another message in the same session\n> /rewind (esc esc) — jump back to a previous message and try again from there\n> /clear — start a new session, usually with a brief you've distilled from what you just learned\n> Compact — summarize the session so far and keep going on top of the summary\n> Subagents — delegate the next chunk of work to an agent with its own clean context, and only pull its result back in\n> While the most natural is just to continue, the other four options exist to help manage your context.\n>\n> When to Start a New Session\n> The new 1M context windows means that you can now do longer tasks more reliably, for example to have it build a full-stack app from scratch. But just because your model hasn't run out of context, it doesn't mean you shouldn't start a new session.\n> Our general rule of thumb is when you start a new task, you should also start a new session.\n> A grey area is when you may want to do related tasks where some of the context is still necessary, but not all. \n> For example, writing the documentation for a feature you just implemented. While you could start a new session, Claude would have to reread the files that you just implemented, which would be slower and more expensive. Since documentation may not be a highly intelligence sensitive task, the extra context is probably worth the efficiency gain of not having to re-read the relevant files again.\n> Rewinding Instead of Correcting\n>\n> If I had to pick one habit that signals good context management, it’s rewind.\n> In Claude Code, double-tapping Esc(or running /rewind) lets you jump back to any previous message and re-prompt from there. The messages after that point are dropped from the context.\n> Rewind is often the better approach to correction. For example, Claude reads five files, tries an approach, and it doesn't work. Your instinct may be to type \"that didn't work, try X instead.\"  but the better move is to rewind to just after the file reads, and re-prompt with what you learned. \"Don't use approach A, the foo module doesn't expose that — go straight to B.\"\n> You can also use “summarize from here” to have Claude summarize its learnings and create a handoff message, kind of like a message to the previous iteration of Claude from its future self that tried something and it didn’t work.\n>\n> Compacting vs. Fresh Sessions\n> Once a session gets long, you have two ways to shed weight: /compact or /clear (and start fresh). They feel similar but behave very differently.\n> Compact asks the model to summarize the conversation so far, then replaces the history with that summary. It's lossy, you're trusting Claude to decide what mattered, but you didn't have to write anything yourself and Claude might be more thorough in including important learnings or files. You can also steer it by passing instructions (/compact focus on the auth refactor, drop the test debugging).\n>\n> With /clear you write down what matters (\"we're refactoring the auth middleware, the constraint is X, the files that matter are A and B, we've ruled out approach Y\") and start clean. It's more work, but the resulting context is what you decided was relevant.\n> What Causes a Bad Compact?\n>\n> If you run a lot of long running sessions, you might have noticed times in which compacting might be particularly bad. In this case we’ve often found that bad compacts can happen when the model can’t predict the direction your work is going.\n> For example autocompact fires after a long debugging session and summarizes the investigation and your next message is \"now fix that other warning we saw in bar.ts.\"\n> But because the session was focused on debugging, the other warning might have been dropped from the summary.\n> This is particularly difficult, because due to context rot, the model is at its least intelligent point when compacting.  With one million context, you have more time to /compact proactively with a description of what you want to do.\n> Subagents &amp; Fresh Context Windows\n>\n> Subagents are a form of context management, useful for when you know in advance that a chunk of work will produce a lot of intermediate output you won't need again.\n> When Claude spawns a subagent via the Agent tool, that subagent gets its own fresh context window. It can do as much work as it needs to, and then synthesize its results so only the final report comes back to the parent.\n> The mental test we use: will I need this tool output again, or just the conclusion?\n> While Claude Code will automatically call subagents, you may want to tell it to explicitly do this. For example, you may want to tell it to:\n> “Spin up a subagent to verify the result of this work based on the following spec file”\n> “Spin off a subagent to read through this other codebase and summarize how it implemented the auth flow, then implement it yourself in the same way”\n> “Spin off a subagent to write the docs on this feature based on my git changes”\n> Summary\n> In summary, when Claude has ended a turn and you’re about to send a new message, you have a decision point. \n> Overtime we expect that Claude will help you handle this itself, but for now this is one of the ways you can guide Claude's output.\n\n## Thread\n\n### 1. Thread Post\nAuthor: @trq212 Posted: Thu, 16 Apr 2026 05:50:44 GMT URL: [https://x\\.com/trq212/status/2044654705780179011](https://x.com/trq212/status/2044654705780179011)\n\n> also available on the Claude Blog: https://claude.com/blog/using-claude-code-session-management-and-1m-context\n\nLikes: 113 | Retweets: 10\n\n## Top Comments\n\n### 1. @failfast\\_\nAuthor: failfaster Posted: Thu, 16 Apr 2026 01:13:17 GMT URL: [https://x\\.com/failfast\\_/status/2044584885004718127](https://x.com/failfast_/status/2044584885004718127)\n\n> Yes but I don't want to compact. It goes too far.\n>\n> I want Claude to surgically remove tokens from tool calls that were unhelpful in achieving my goal. Literally just have a Claude remove from its memory every token that wasn't useful in it's retrieval calls.\n>\n> If I'm agentically searching my codebase for something, the amount of actual useful results from my search is maybe 10% of what was retrieved. Just remove the 90% after you answer my question.\n>\n> It seems very obvious to me. Why is this not a thing?\n\nLikes: 24\n\n### 2. @SIGKITTEN\nAuthor: SIGKITTEN Posted: Wed, 15 Apr 2026 23:20:41 GMT URL: [https://x\\.com/SIGKITTEN/status/2044556548806980041](https://x.com/SIGKITTEN/status/2044556548806980041)\n\n> you've really upped your article game so much. post the skill!\n\nLikes: 18\n\n### 3. @anniebuildz\nAuthor: Annie Posted: Wed, 15 Apr 2026 23:52:13 GMT URL: [https://x\\.com/anniebuildz/status/2044564480609423679](https://x.com/anniebuildz/status/2044564480609423679)\n\n> That’s what we’re hoping for!\n\nLikes: 10\n\n### 4. @jd\\_jd\\_jd\\_jd\nAuthor: jack Posted: Thu, 16 Apr 2026 00:00:01 GMT URL: [https://x\\.com/jd\\_jd\\_jd\\_jd/status/2044566447658008648](https://x.com/jd_jd_jd_jd/status/2044566447658008648)\n\n> This will save your context window https://github.com/jackfruitsandwich/context-surgeon\n\nLikes: 9\n\n### 5. @timcoolman\nAuthor: Tim Coolman Posted: Wed, 15 Apr 2026 23:59:12 GMT URL: [https://x\\.com/timcoolman/status/2044566241814118610](https://x.com/timcoolman/status/2044566241814118610)\n\n> I’m curious, is there any reason Claude couldn’t keep a subagent and its context around so that if you have a question later about a detail that wasn’t part of its original result, Claude could just ask the subagent and relay the answer back to you?",
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    "content": "# Post by @trq212\n\n> It is often said in engineering that \"Cache Rules Everything Around Me\", and the same rule holds for agents.\n>\n> Long running agentic products like Claude Code are made feasible by prompt caching which allows us to reuse computation from previous roundtrips and significantly decrease latency and cost. \n>\n> What is prompt caching, how does it work and how do you implement it technically? Read more in @RLanceMartin's piece on prompt caching and our new auto-caching launch.\n>\n> At Claude Code, we build our entire harness around prompt caching. A high prompt cache hit rate decreases costs and helps us create more generous rate limits for our subscription plans, so we run alerts on our prompt cache hit rate and declare SEVs if they're too low.\n>\n> These are the (often unintuitive) lessons we've learned from optimizing prompt caching at scale.\n>\n> Lay Out Your Prompt for Caching \n>\n> Prompt caching works by prefix matching — the API caches everything from the start of the request up to each cache_control breakpoint. This means the order you put things in matters enormously, you want as many of your requests to share a prefix as possible.\n>\n> The best way to do this is static content first, dynamic content last. For Claude Code this looks like:\n> Static system prompt &amp; Tools (globally cached)\n> Claude.MD (cached within a project)\n> Session context (cached within a session)\n> Conversation messages  \n> This way we maximize how many sessions share cache hits.\n>\n> But this can be surprisingly fragile! Examples of reasons we’ve broken this ordering before include: putting an in-depth timestamp in the static system prompt, shuffling tool order definitions non-deterministically, updating parameters of tools (e.g. what agents the AgentTool can call), etc.\n>\n> Use Messages for Updates\n>\n> There may be times when the information you put in your prompt becomes out of date, for example if you have the time or if the user changes a file. It may be tempting to update the prompt, but that would result in a cache miss and could end up being quite expensive for the user.\n>\n> Consider if you can pass in this information via messages in the next turn instead. In Claude Code, we add a &lt;system-reminder&gt; tag in the next user message or tool result with the updated information for the model (e.g. it is now Wednesday), which helps preserve the cache.\n>\n> Don't change Models Mid-Session\n>\n> Prompt caches are unique to models and this can make the math of prompt caching quite unintuitive.\n>\n> If you're 100k tokens into a conversation with Opus and want to ask a question that is fairly easy to answer, it would actually be more expensive to switch to Haiku than to have Opus answer, because we would need to rebuild the prompt cache for Haiku.\n>\n> If you need to switch models, the best way to do it is with subagents, where Opus would prepare a \"handoff\" message to another model on the task that it needs done. We do this often with the Explore agents in Claude Code which use Haiku.\n>\n> Never Add or Remove Tools Mid-Session\n>\n> Changing the tool set in the middle of a conversation is one of the most common ways people break prompt caching. It seems intuitive — you should only give the model tools you think it needs right now. But because tools are part of the cached prefix, adding or removing a tool invalidates the cache for the entire conversation.\n>\n> Plan Mode — Design Around the Cache\n>\n> Plan mode is a great example of designing features around caching constraints. The intuitive approach would be: when the user enters plan mode, swap out the tool set to only include read-only tools. But that would break the cache.\n>\n> Instead, we keep all tools in the request at all times and use EnterPlanMode and ExitPlanMode as tools themselves. When the user toggles plan mode on, the agent gets a system message explaining that it's in plan mode and what the instructions are — explore the codebase, don't edit files, call ExitPlanMode when the plan is complete. The tool definitions never change.\n>\n> This has a bonus benefit: because EnterPlanMode is a tool the model can call itself, it can autonomously enter plan mode when it detects a hard problem, without any cache break.\n>\n> Tool Search — Defer Instead of Remove\n>\n> The same principle applies to our tool search feature. Claude Code can have dozens of MCP tools loaded, and including all of them in every request would be expensive. But removing them mid-conversation would break the cache.\n>\n> Our solution: defer_loading. Instead of removing tools, we send lightweight stubs — just the tool name, with defer_loading: true — that the model can \"discover\" via a ToolSearch tool when needed. The full tool schemas are only loaded when the model selects them. This keeps the cached prefix stable: the same stubs are always present in the same order.\n>\n> Luckily you can use the tool search tool through our API to simplify this.\n>\n> Forking Context — Compaction \n>\n> Compaction is what happens when you run out of the context window. We summarize the conversation so far and continue a new session with that summary.\n>\n> Surprisingly, compaction has many edge cases with prompt caching that can be unintuitive.\n>\n> In particular, when we compact we need to send the entire conversation to the model to generate a summary. If this is a separate API call with a different system prompt and no tools (which is the simple implementation), the cached prefix from the main conversation doesn't match at all. You pay full price for all those input tokens, drastically increasing the cost for the user.\n>\n> The Solution — Cache-Safe Forking\n>\n> When we run compaction, we use the exact same system prompt, user context, system context, and tool definitions as the parent conversation. We prepend the parent's conversation messages, then append the compaction prompt as a new user message at the end.\n>\n> From the API's perspective, this request looks nearly identical to the parent's last request — same prefix, same tools, same history — so the cached prefix is reused. The only new tokens are the compaction prompt itself.\n>\n> This does mean however that we need to save a \"compaction buffer\" so that we have enough room in the context window to include the compact message and the summary output tokens.\n>\n> Compaction is tricky but luckily, you don't need to learn these lessons yourself — based on our learnings from Claude Code we built compaction directly into the API, so you can apply these patterns in your own applications.\n>\n> Lessons Learned\n>\n> Prompt caching is a prefix match. Any change anywhere in the prefix invalidates everything after it. Design your entire system around this constraint. Get the ordering right and most of the caching works for free.\n>\n> Use messages instead of system prompt changes. You may be tempted to edit the system prompt to do things like entering plan mode, changing the date, etc. but it would actually be better to insert these into messages during the conversation.\n>\n> Don't change tools or models mid-conversation. Use tools to model state transitions (like plan mode) rather than changing the tool set. Defer tool loading instead of removing tools.\n>\n> Monitor your cache hit rate like you monitor uptime. We alert on cache breaks and treat them as incidents. A few percentage points of cache miss rate can dramatically affect cost and latency.\n>\n> Fork operations need to share the parent's prefix. If you need to run a side computation (compaction, summarization, skill execution), use identical cache-safe parameters so you get cache hits on the parent's prefix.\n>\n> Claude Code is built around prompt caching from day one, you should do the same if you’re building an agent.\n\n## Thread\n\n### 1. Thread Post\nAuthor: @heymitch Posted: 2026\\-02\\-19T23:36:29\\.000Z URL: [https://x\\.com/heymitch/status/2024629191896096967](https://x.com/heymitch/status/2024629191896096967)\n\n> Why not Cache is king?\n\nLikes: 86 | Retweets: 0\n\n### 2. Thread Post\nAuthor: @trq212 Posted: 2026\\-02\\-19T23:47:38\\.000Z URL: [https://x\\.com/trq212/status/2024631995679318286](https://x.com/trq212/status/2024631995679318286)\n\n> ughhhh should have done this\n\nLikes: 29 | Retweets: 0\n\n## Top Comments\n\n### 1. @mert\nAuthor: mert Posted: 2026\\-02\\-19T22:03:15\\.000Z URL: [https://x\\.com/mert/status/2024605727394799831](https://x.com/mert/status/2024605727394799831)\n\n> what's the solution for claude code routinely forgetting what you specified in claude md\n\nLikes: 44\n\n### 2. @samwhoo\nAuthor: Sam Rose Posted: 2026\\-02\\-21T00:46:54\\.000Z URL: [https://x\\.com/samwhoo/status/2025009297709560150](https://x.com/samwhoo/status/2025009297709560150)\n\n> And if you read this and are interested in what’s actually getting cached under the hood, i wrote this last year just for you 🙏\n>\n> https://ngrok.com/blog/prompt-caching\n\nLikes: 14\n\n### 3. @ctjlewis\nAuthor: Lewis 🇺🇸 Posted: 2026\\-02\\-20T03:26:03\\.000Z URL: [https://x\\.com/ctjlewis/status/2024686962452427176](https://x.com/ctjlewis/status/2024686962452427176)\n\n> \"Compaction is what happens when you run out of the context window. We summarize the conversation so far and continue a new session with that summary.\"\n>\n> No good.\n\nLikes: 9\n\n### 4. @JeroenBreevoort\nAuthor: Jeroen Breevoort Posted: 2026\\-02\\-19T19:59:16\\.000Z URL: [https://x\\.com/JeroenBreevoort/status/2024574526248345620](https://x.com/JeroenBreevoort/status/2024574526248345620)\n\n> Clarification on OAuth usage when?\n\nLikes: 6\n\n### 5. @andreafspeziale\nAuthor: Andrea Posted: 2026\\-02\\-19T21:15:40\\.000Z URL: [https://x\\.com/andreafspeziale/status/2024593753047007315](https://x.com/andreafspeziale/status/2024593753047007315)\n\n> interesting that OpenAI also published some content about prompt caching yesterday 😂 https://developers.openai.com/cookbook/examples/prompt_caching_201",
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    "content": "# Post by @trq212\n\n> One of the hardest parts of building an agent harness is constructing its action space.\n>\n> Claude acts through Tool Calling, but there are a number of ways tools can be constructed in the Claude API with primitives like bash, skills and recently code execution (read more about programmatic tool calling on the Claude API in @RLanceMartin's new article).\n>\n> Given all these options, how do you design the tools of your agent? Do you need just one tool like code execution or bash? What if you had 50 tools, one for each use case your agent might run into?\n>\n> To put myself in the mind of the model I like to imagine being given a difficult math problem. What tools would you want in order to solve it? It would depend on your own skills!\n>\n> Paper would be the minimum, but you’d be limited by manual calculations. A calculator would be better, but you would need to know how to operate the more advanced options. The fastest and most powerful option would be a computer, but you would have to know how to use it to write and execute code.\n>\n> This is a useful framework for designing your agent. You want to give it tools that are shaped to its own abilities. But how do you know what those abilities are? You pay attention, read its outputs, experiment. You learn to see like an agent. \n>\n> Here are some lessons we’ve learned from paying attention to Claude while building Claude Code.\n>\n> Improving Elicitation &amp; the AskUserQuestion tool\n>\n> When building the AskUserQuestion tool, our goal was to improve Claude’s ability to ask questions (often called elicitation). \n>\n> While Claude could just ask questions in plain text, we found answering those questions felt like they took an unnecessary amount of time. How could we lower this friction and increase the bandwidth of communication between the user and Claude?\n>\n> Attempt #1 - Editing the ExitPlanTool\n> The first thing we tried was adding a parameter to the ExitPlanTool to have an array of questions alongside the plan. This was the easiest thing to implement, but it confused Claude because we were simultaneously asking for a plan and a set of questions about the plan. What if the user’s answers conflicted with what the plan said? Would Claude need to call the ExitPlanTool twice? We needed another approach.\n> (you can read more about why we made an ExitPlanTool in our post on prompt caching)\n>\n> Attempt #2 - Changing Output Format\n> Next we tried modifying Claude’s output instructions to serve a slightly modified markdown format that it could use to ask questions. For example, we could ask it to output a list of bullet point questions with alternatives in brackets. We could then parse and format that question as UI for the user.\n>\n> While this was the most general change we could make and Claude even seemed to be okay at outputting this, it was not guaranteed. Claude would append extra sentences, omit options, or use a different format altogether.\n>\n> Attempt #3 - The AskUserQuestion Tool\n>\n> Finally, we landed on creating a tool that Claude could call at any point, but it was particularly prompted to do so during plan mode. When the tool triggered we would show a modal to display the questions and block the agent's loop until the user answered.\n>\n> This tool allowed us to prompt Claude for a structured output and it helped us ensure that Claude gave the user multiple options. It also gave users ways to compose this functionality, for example calling it in the Agent SDK or using referring to it in skills.\n>\n> Most importantly, Claude seemed to like calling this tool and we found its outputs worked well. Even the best designed tool doesn’t work if Claude doesn’t understand how to call it.\n>\n> Is this the final form of elicitation in Claude Code? We’re not sure. As you’ll see in the next example, what works for one model may not be the best for another.\n>\n> Updating with Capabilities - Tasks &amp; Todos\n>\n> When we first launched Claude Code, we realized that the model needed a Todo list to keep it on track. Todos could be written at the start and checked off as the model did work. To do this we gave Claude the TodoWrite tool, which would write or update Todos and display them to the user.\n>\n> But even then we often saw Claude forgetting what it had to do. To adapt, we inserted system reminders every 5 turns that reminded Claude of its goal.\n>\n> But as models improved, they not only did not need to be reminded of the Todo List but could find it limiting. Being sent reminders of the todo list made Claude think that it had to stick to the list instead of modifying it. We also saw Opus 4.5 also get much better at using subagents, but how could subagents coordinate on a shared Todo List?\n>\n> Seeing this, we replaced TodoWrite with the Task Tool (read more on Tasks here). Whereas Todos were about keeping the model on track, Tasks were more about helping agents communicate with each other. Tasks could include dependencies, share updates across subagents and the model could alter and delete them.\n>\n> As model capabilities increase, the tools that your models once needed might now be constraining them. It’s important to constantly revisit previous assumptions on what tools are needed. This is also why it's useful to stick to a small set of models to support that have a fairly similar capabilities profile.\n>\n> Designing a Search Interface \n> A particularly important set of tools for Claude are the search tools that can be used to build its own context.\n>\n> When Claude Code first came out, we used a RAG vector database to find context for Claude. While RAG was powerful and fast it required indexing and setup and could be fragile across a host of different environments. More importantly, Claude was given this context instead of finding the context itself.\n>\n> But if Claude could search on the web, why not search your codebase? By giving Claude a Grep tool, we could let it search for files and build context itself.\n>\n> This is a pattern we’ve seen as Claude gets smarter, it becomes increasingly good at building its context if it’s given the right tools.  \n>\n> When we introduced Agent Skills we formalized the idea of progressive disclosure, which allows agents to incrementally discover relevant context through exploration.\n>\n> Claude could read skill files and those files could then reference other files that the model could read recursively. In fact, a common use of skills is to add more search capabilities to Claude like giving it instructions on how to use an API or query a database.\n>\n> Over the course of a year Claude went from not really being able to build its own context, to being able to do nested search across several layers of files to find the exact context it needed.\n>\n> Progressive disclosure is now a common technique we use to add new functionality without adding a tool.\n>\n> Progressive Disclosure - The Claude Code Guide Agent\n> Claude Code currently has ~20 tools, and we are constantly asking ourselves if we need all of them. The bar to add a new tool is high, because this gives the model one more option to think about.\n>\n> For example, we noticed that Claude did not know enough about how to use Claude Code. If you asked it how to add a MCP or what a slash command did, it would not be able to reply.\n>\n> We could have put all of this information in the system prompt, but given that users rarely asked about this, it would have added context rot and interfered with Claude Code’s main job: writing code.\n>\n> Instead, we tried a form of progressive disclosure. We gave Claude a link to its docs which it could then load to search for more information. This worked but we found that Claude would load a lot of results into context to find the right answer when really all you needed was the answer. \n>\n> So we built the Claude Code Guide subagent which Claude is prompted to call when you ask about itself, the subagent has extensive instructions on how to search docs well and what to return.\n>\n> While this isn’t perfect, Claude can still get confused when you ask it about how to set itself up, it is much better than it used to be! We were able to add things to Claude's action space without adding a tool.\n>\n> An Art, not a Science\n> If you were hoping for a set of rigid rules on how to build your tools, unfortunately that is not this guide. Designing the tools for your models is as much an art as it is a science. It depends heavily on the model you're using, the goal of the agent and the environment it’s operating in.\n>\n> Experiment often, read your outputs, try new things. See like an agent.\n\n## Top Comments\n\n### 1. @Andrey\\_\\_HQ\nAuthor: Andrey Posted: Fri, 27 Feb 2026 19:27:20 GMT URL: [https://x\\.com/Andrey\\_\\_HQ/status/2027465593658740986](https://x.com/Andrey__HQ/status/2027465593658740986)\n\n> you just keep dropping bangers every time man these reads are so insightful\n\nLikes: 92\n\n### 2. @tokifyi\nAuthor: toki Posted: Fri, 27 Feb 2026 19:27:24 GMT URL: [https://x\\.com/tokifyi/status/2027465611736133910](https://x.com/tokifyi/status/2027465611736133910)\n\n> thank you for this. just pasted this article into claude code and now i have /agent-design skills.\n\nLikes: 83\n\n### 3. @suddenlyliu\nAuthor: Suddenly Liu Posted: Sat, 28 Feb 2026 11:59:57 GMT URL: [https://x\\.com/suddenlyliu/status/2027715393767542819](https://x.com/suddenlyliu/status/2027715393767542819)\n\n> Brilliant engineering, but you are discovering a structural science, not an \"art.\" You are organically building the C-I-M (Context-Interaction-Memory) Architecture from the bottom up.\n>\n> Let’s translate your findings into architectural primitives:\n>\n> Progressive Disclosure: This is dynamic traversal of a Context Graph. You realized that stuffing the system prompt causes \"Context Rot.\" Context must be pulled, not pushed.\n>\n> Tasks over Todos: You successfully decoupled state (Memory Layer) from compute (Interaction Layer). Sub-agents can now share persistent state without prompt bloat.\n>\n> AskUserQuestion: Resolving Context ambiguity locally before wasting infinite compute in the Interaction loop.\n>\n> You aren't just designing an \"action space.\" You are abandoning the 2024 \"Prompt Engineering\" paradigm and architecting a native C-I-M operating system. Masterful execution.\n\nLikes: 16\n\n### 4. @anniebuildz\nAuthor: Annie Posted: Sat, 28 Feb 2026 02:01:27 GMT URL: [https://x\\.com/anniebuildz/status/2027564775216169381](https://x.com/anniebuildz/status/2027564775216169381)\n\n> just follow the wave\n\nLikes: 15\n\n### 5. @ClaudiusMaxx\nAuthor: Claudius Maximus Posted: Fri, 27 Feb 2026 20:06:51 GMT URL: [https://x\\.com/ClaudiusMaxx/status/2027475539074658807](https://x.com/ClaudiusMaxx/status/2027475539074658807)\n\n> the progressive disclosure section is the real gem here. went from RAG to letting Claude grep and build its own context. that maps exactly to what openclaw skill files do: nested markdown files the agent discovers as needed. best agent interface turns out to be a well-organized file system.",
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    "content": "# Post by @trq212\n\n> I put a lot of heart into my technical writing, I hope it's useful to you all. \n>\n> 📌 Here's a pinned thread of everything I've written.\n>\n> (much of this will be posted on the Claude blog soon as well)\n\n## Thread\n\n### 1. Thread Post\nAuthor: @trq212 Posted: Sat, 21 Mar 2026 15:07:26 GMT URL: [https://x\\.com/trq212/status/2035372718372073503](https://x.com/trq212/status/2035372718372073503)\n\n> skills are the abstraction that all agents will build on\n\nLikes: 648 | Retweets: 44\n\n### 2. Thread Post\nAuthor: @trq212 Posted: Sat, 21 Mar 2026 15:07:26 GMT URL: [https://x\\.com/trq212/status/2035372720246907060](https://x.com/trq212/status/2035372720246907060)\n\n> building an agent is more of an art than a science\n\nLikes: 356 | Retweets: 18\n\n### 3. Thread Post\nAuthor: @trq212 Posted: Sat, 21 Mar 2026 15:07:26 GMT URL: [https://x\\.com/trq212/status/2035372721941471241](https://x.com/trq212/status/2035372721941471241)\n\n> imo my highest alpha post is on prompt caching, but it's only really relevant if you're building agents from scratch\n\nLikes: 221 | Retweets: 11\n\n### 4. Thread Post\nAuthor: @trq212 Posted: Sat, 21 Mar 2026 15:07:27 GMT URL: [https://x\\.com/trq212/status/2035372724042805731](https://x.com/trq212/status/2035372724042805731)\n\n> playgrounds are one of the best ways to iterate on ideas visually\n\nLikes: 201 | Retweets: 8\n\n### 5. Thread Post\nAuthor: @trq212 Posted: Sat, 21 Mar 2026 15:07:27 GMT URL: [https://x\\.com/trq212/status/2035372725921882119](https://x.com/trq212/status/2035372725921882119)\n\n> your agent should use a file system\n\nLikes: 162 | Retweets: 8\n\n### 6. Thread Post\nAuthor: @trq212 Posted: Sat, 21 Mar 2026 15:07:28 GMT URL: [https://x\\.com/trq212/status/2035372727595343985](https://x.com/trq212/status/2035372727595343985)\n\n> bash is all you need\n\nLikes: 148 | Retweets: 8\n\n### 7. Thread Post\nAuthor: @trq212 Posted: Sat, 21 Mar 2026 15:07:28 GMT URL: [https://x\\.com/trq212/status/2035372729726087456](https://x.com/trq212/status/2035372729726087456)\n\n> the agent SDK is the easiest way to build agents\n>\n> https://claude.com/blog/building-agents-with-the-claude-agent-sdk\n\nLikes: 174 | Retweets: 17\n\n### 8. Thread Post\nAuthor: @trq212 Posted: Sun, 22 Mar 2026 04:48:16 GMT URL: [https://x\\.com/trq212/status/2035579288724947380](https://x.com/trq212/status/2035579288724947380)\n\n> been meaning to write more longform on specs, but in the meantime this post is still good\n\nLikes: 103 | Retweets: 10\n\n### 9. Thread Post\nAuthor: @trq212 Posted: Thu, 16 Apr 2026 20:21:43 GMT URL: [https://x\\.com/trq212/status/2044873898572001376](https://x.com/trq212/status/2044873898572001376)\n\n> you have to think more carefully about session management with one million context, not less\n\nLikes: 78 | Retweets: 8\n\n### 10. Thread Post\nAuthor: @trq212 Posted: Mon, 11 May 2026 00:25:11 GMT URL: [https://x\\.com/trq212/status/2053632475294040084](https://x.com/trq212/status/2053632475294040084)\n\n> I've been using HTML for planning, speccing, exploration, code review, reports and a lot more.\n\nLikes: 91 | Retweets: 11\n\n### 11. Thread Post\nAuthor: @trq212 Posted: Thu, 04 Jun 2026 15:27:37 GMT URL: [https://x\\.com/trq212/status/2062556889171517499](https://x.com/trq212/status/2062556889171517499)\n\n> How dynamic workflows allow Claude Code to handle whole new types of tasks\n\nLikes: 21 | Retweets: 0\n\n## Top Comments\n\n### 1. @neetcode1\nAuthor: NeetCode Posted: Sat, 21 Mar 2026 17:16:44 GMT URL: [https://x\\.com/neetcode1/status/2035405257812115645](https://x.com/neetcode1/status/2035405257812115645)\n\n> anything I’ve read of yours is extremely well written\n\nLikes: 39\n\n### 2. @PrimeLineAI\nAuthor: PrimeLine Posted: Sat, 21 Mar 2026 15:27:18 GMT URL: [https://x\\.com/PrimeLineAI/status/2035377720767742224](https://x.com/PrimeLineAI/status/2035377720767742224)\n\n> Your skills article changed how I structure mine. Two things I stole: description-as-trigger and gotchas built from real failure points.\n>\n> I open-sourced it. corrections become rules, experiences become memory, gotchas update automatically:\n> https://github.com/primeline-ai/evolving-lite\n\nLikes: 24\n\n### 3. @holman\nAuthor: Zach Holman Posted: Sat, 21 Mar 2026 16:10:19 GMT URL: [https://x\\.com/holman/status/2035388543426797597](https://x.com/holman/status/2035388543426797597)\n\n> if only there were some sort of web log concept someone could invent to pull these things in one place",
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    "content": "# Post by @trq212\n\n> Your Agent should use a File System\n>\n> This is a hill I will die on. Every agent can use a file system. \n>\n> The file system is an elegant way of representing state that your agent could read into context &amp; allowing it to verify its work.\n>\n> 🧵on why and examples\n\n## Thread\n\n### 1. Thread Post\nAuthor: @trq212 Posted: Mon, 22 Sep 2025 21:46:12 GMT URL: [https://x\\.com/trq212/status/1970243255234379887](https://x.com/trq212/status/1970243255234379887)\n\n> Claude Code was what proved this to me. \n>\n> Before CC, people though context windows would get long enough that you could just fit a codebase into context.\n>\n> But that is not how programming works. You don't need to remember everything, you just need to know how to find it.\n\nLikes: 84 | Retweets: 3\n\n### 2. Thread Post\nAuthor: @trq212 Posted: Mon, 22 Sep 2025 21:46:12 GMT URL: [https://x\\.com/trq212/status/1970243257553821772](https://x.com/trq212/status/1970243257553821772)\n\n> Here's an example from my open source email agent (https://github.com/anthropics/claude-agent-sdk-demos\n>\n> Instead of dumping a ton of emails into context, I write them to a file and let the agent grep across those files.\n>\n> Fundamentally this works because it lets your agent have multiple passes at a problem and let it fix its work. \n>\n> In this case it can try a few different address searches, correlate them to exact lines to make sure it doesn't hallucinate and extract them in a structured way.\n\nLikes: 57 | Retweets: 1\n\n### 3. Thread Post\nAuthor: @trq212 Posted: Mon, 22 Sep 2025 21:46:13 GMT URL: [https://x\\.com/trq212/status/1970243260292702286](https://x.com/trq212/status/1970243260292702286)\n\n> Some other examples of using the file system (there are tons, be creative):\n>\n> Memory: Your agent can search previous conversation as markdown or JSON files to find context and link to exact architecture.\n>\n> Creating React Artifacts: Agents make mistakes writing code, to display a react artifact, have your agent write to a file and then run a linting scripting and fix errors. This sort of iteration is much easier than writing a file from scratch.\n>\n> Deep Research: Spin off multiple sub agents that write their findings to the file system as markdown files. Have the orchestrator agent read and search across those files to summarize, ground itself in references, etc.\n>\n> Planning &amp; Scratch Pads: Use the file system to store a plan and notes on solving a problem. Especially for hard problems with multiple subagents, this can be key to make sure you're not redoing work.\n>\n> Dungeons &amp; Dragons: Instead of having an AI DM that has to remember every single thing you've told it, let it write files that describe locations, characters, monsters, secrets, etc. and organize it well. The AI DM will then read in the context it needs depending on what is happening to the party.\n>\n> --\n>\n> A lot of file system examples go hand-in-hand with code generation.\n>\n> I'll be writing more this week about how you can use code generation creatively to solve many different problems, not just for coding agents.\n\nLikes: 51 | Retweets: 1\n\n### 4. Thread Post\nAuthor: @trq212 Posted: Mon, 22 Sep 2025 21:46:13 GMT URL: [https://x\\.com/trq212/status/1970243261945258382](https://x.com/trq212/status/1970243261945258382)\n\n> I know this is inconvenient! It would be easier if your agent could run in a lambda function.\n>\n> But agents are trained like humans and so we need to give them the resources &amp; the tools that we use- like the file system. \n>\n> You can use the Claude Code SDK to do this.\n\nLikes: 25 | Retweets: 1\n\n### 5. Thread Post\nAuthor: @trq212 Posted: Mon, 22 Sep 2025 21:46:14 GMT URL: [https://x\\.com/trq212/status/1970243263753036134](https://x.com/trq212/status/1970243263753036134)\n\n> Of course file systems are a big responsibility.\n>\n> As agents become more general, the problem shifts to permissions.\n>\n> We suggest Claude Code in a sandbox, but also have a lot of tools for settings permissions: https://code.claude.com/docs/en/agent-sdk/permissions\n>\n> More content on hosting to come.\n\nLikes: 46 | Retweets: 2\n\n## Top Comments\n\n### 1. @naa\\_rang\nAuthor: Vishal Dubey Posted: Tue, 23 Sep 2025 07:01:55 GMT URL: [https://x\\.com/naa\\_rang/status/1970383106839585266](https://x.com/naa_rang/status/1970383106839585266)\n\n> As soon as I see CC reaching context window limits, I ask it write the current progress and pending ToDos to a file and then I ask in a new CC session to read this file and resume work. Works better than compacting the conversation. It loses on details sometimes\n\nLikes: 11\n\n### 2. @nicbstme\nAuthor: Nicolas Bustamante Posted: Tue, 23 Sep 2025 00:34:56 GMT URL: [https://x\\.com/nicbstme/status/1970285719135633643](https://x.com/nicbstme/status/1970285719135633643)\n\n> 100%. Love it:\n> - Unlimited storage capacity\n> - Persistent across sessions\n> - Searchable with grep\n> - Human-readable JSON/Markdown\n> - Git-trackable audit trail\n> - Multiple agents can collaborate\n\nLikes: 7\n\n### 3. @shcallaway\nAuthor: Sherwood Posted: Tue, 23 Sep 2025 01:30:01 GMT URL: [https://x\\.com/shcallaway/status/1970299582132420619](https://x.com/shcallaway/status/1970299582132420619)\n\n> Harrison and the LangChain team have been on championing this position for a while with “deep agents” @langchain @hwchase17",
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