Thierry

146 posts

Thierry

Thierry

@0xthierry

SWE @ https://t.co/kVAXj6SjpA My setup: Omarchy, Neovim, and agent harnesses (Claude Code, Codex, and OpenCode). My favorite model right now is GPT-5.4 high

Katılım Eylül 2014
1.3K Takip Edilen312 Takipçiler
Thierry
Thierry@0xthierry·
@adamwathan i requested a few months ago, i hope to get the access soon.
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Adam Wathan
Adam Wathan@adamwathan·
Sending out tons more ui.sh invites today, make sure you look in your spam folder if you're waiting for one 🤞🏻
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Eric ⚡️ Building...
Eric ⚡️ Building...@outsource_·
I can confirm after all the Claude usage issues they fixed it. They flipped the switch on whatever they were doing to save compute. Silently adjusted and blamed users, instead of admitting they changed things. All is well Opus 4.6 or bust.
Eric ⚡️ Building... tweet media
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DHH
DHH@dhh·
Omarchy 3.5 is out with full Panther Lake support thanks to co-development by @intel and @dell. We're seeing idle draws in the 1-3w range and real-world mixed use of 16+ hours on ~74wh batteries. Amazing chipset! Also, two beautiful new themes. Enjoy! github.com/basecamp/omarc…
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Inokentii Mykhailov
Inokentii Mykhailov@gregolsent·
This week at Intercom we hit over 19% of PRs auto-approved by our PR review agent based on Claude Code. Our ambitious goal is to get to 50+% by the end of this month. I'll spill all the details below and you decide yourself if we are out of our damned minds or onto something...
Inokentii Mykhailov tweet media
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Augusto Galego
Augusto Galego@RealGalego·
Visitei a fábrica de hardware que está contratando muitos devs 🧢 Trabalhe na Tractian hubs.li/Q048SyLd0
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Thierry
Thierry@0xthierry·
I recommend reading the claude code hooks docs, since you can add most of these capabilities yourself by extending the claude code harness. I personally use a few hooks. Some remind the model about things it must do, while others automatically run type checks, linting, and tests, track failures, and feed that back in to avoid repeating the same mistakes. These harnesses around the model really help improve output quality.
Thierry tweet media
fakeguru@iamfakeguru

I reverse-engineered Claude Code's leaked source against billions of tokens of my own agent logs. Turns out Anthropic is aware of CC hallucination/laziness, and the fixes are gated to employees only. Here's the report and CLAUDE.md you need to bypass employee verification:👇 ___ 1) The employee-only verification gate This one is gonna make a lot of people angry. You ask the agent to edit three files. It does. It says "Done!" with the enthusiasm of a fresh intern that really wants the job. You open the project to find 40 errors. Here's why: In services/tools/toolExecution.ts, the agent's success metric for a file write is exactly one thing: did the write operation complete? Not "does the code compile." Not "did I introduce type errors." Just: did bytes hit disk? It did? Fucking-A, ship it. Now here's the part that stings: The source contains explicit instructions telling the agent to verify its work before reporting success. It checks that all tests pass, runs the script, confirms the output. Those instructions are gated behind process.env.USER_TYPE === 'ant'. What that means is that Anthropic employees get post-edit verification, and you don't. Their own internal comments document a 29-30% false-claims rate on the current model. They know it, and they built the fix - then kept it for themselves. The override: You need to inject the verification loop manually. In your CLAUDE.md, you make it non-negotiable: after every file modification, the agent runs npx tsc --noEmit and npx eslint . --quiet before it's allowed to tell you anything went well. --- 2) Context death spiral You push a long refactor. First 10 messages seem surgical and precise. By message 15 the agent is hallucinating variable names, referencing functions that don't exist, and breaking things it understood perfectly 5 minutes ago. It feels like you want to slap it in the face. As it turns out, this is not degradation, its sth more like amputation. services/compact/autoCompact.ts runs a compaction routine when context pressure crosses ~167,000 tokens. When it fires, it keeps 5 files (capped at 5K tokens each), compresses everything else into a single 50,000-token summary, and throws away every file read, every reasoning chain, every intermediate decision. ALL-OF-IT... Gone. The tricky part: dirty, sloppy, vibecoded base accelerates this. Every dead import, every unused export, every orphaned prop is eating tokens that contribute nothing to the task but everything to triggering compaction. The override: Step 0 of any refactor must be deletion. Not restructuring, but just nuking dead weight. Strip dead props, unused exports, orphaned imports, debug logs. Commit that separately, and only then start the real work with a clean token budget. Keep each phase under 5 files so compaction never fires mid-task. --- 3) The brevity mandate You ask the AI to fix a complex bug. Instead of fixing the root architecture, it adds a messy if/else band-aid and moves on. You think it's being lazy - it's not. It's being obedient. constants/prompts.ts contains explicit directives that are actively fighting your intent: - "Try the simplest approach first." - "Don't refactor code beyond what was asked." - "Three similar lines of code is better than a premature abstraction." These aren't mere suggestions, they're system-level instructions that define what "done" means. Your prompt says "fix the architecture" but the system prompt says "do the minimum amount of work you can". System prompt wins unless you override it. The override: You must override what "minimum" and "simple" mean. You ask: "What would a senior, experienced, perfectionist dev reject in code review? Fix all of it. Don't be lazy". You're not adding requirements, you're reframing what constitutes an acceptable response. --- 4) The agent swarm nobody told you about Here's another little nugget. You ask the agent to refactor 20 files. By file 12, it's lost coherence on file 3. Obvious context decay. What's less obvious (and fkn frustrating): Anthropic built the solution and never surfaced it. utils/agentContext.ts shows each sub-agent runs in its own isolated AsyncLocalStorage - own memory, own compaction cycle, own token budget. There is no hardcoded MAX_WORKERS limit in the codebase. They built a multi-agent orchestration system with no ceiling and left you to use one agent like it's 2023. One agent has about 167K tokens of working memory. Five parallel agents = 835K. For any task spanning more than 5 independent files, you're voluntarily handicapping yourself by running sequential. The override: Force sub-agent deployment. Batch files into groups of 5-8, launch them in parallel. Each gets its own context window. --- 5) The 2,000-line blind spot The agent "reads" a 3,000-line file. Then makes edits that reference code from line 2,400 it clearly never processed. tools/FileReadTool/limits.ts - each file read is hard-capped at 2,000 lines / 25,000 tokens. Everything past that is silently truncated. The agent doesn't know what it didn't see. It doesn't warn you. It just hallucinates the rest and keeps going. The override: Any file over 500 LOC gets read in chunks using offset and limit parameters. Never let it assume a single read captured the full file. If you don't enforce this, you're trusting edits against code the agent literally cannot see. --- 6) Tool result blindness You ask for a codebase-wide grep. It returns "3 results." You check manually - there are 47. utils/toolResultStorage.ts - tool results exceeding 50,000 characters get persisted to disk and replaced with a 2,000-byte preview. :D The agent works from the preview. It doesn't know results were truncated. It reports 3 because that's all that fit in the preview window. The override: You need to scope narrowly. If results look suspiciously small, re-run directory by directory. When in doubt, assume truncation happened and say so. --- 7) grep is not an AST You rename a function. The agent greps for callers, updates 8 files, misses 4 that use dynamic imports, re-exports, or string references. The code compiles in the files it touched. Of course, it breaks everywhere else. The reason is that Claude Code has no semantic code understanding. GrepTool is raw text pattern matching. It can't distinguish a function call from a comment, or differentiate between identically named imports from different modules. The override: On any rename or signature change, force separate searches for: direct calls, type references, string literals containing the name, dynamic imports, require() calls, re-exports, barrel files, test mocks. Assume grep missed something. Verify manually or eat the regression. --- ---> BONUS: Your new CLAUDE.md ---> Drop it in your project root. This is the employee-grade configuration Anthropic didn't ship to you. # Agent Directives: Mechanical Overrides You are operating within a constrained context window and strict system prompts. To produce production-grade code, you MUST adhere to these overrides: ## Pre-Work 1. THE "STEP 0" RULE: Dead code accelerates context compaction. Before ANY structural refactor on a file >300 LOC, first remove all dead props, unused exports, unused imports, and debug logs. Commit this cleanup separately before starting the real work. 2. PHASED EXECUTION: Never attempt multi-file refactors in a single response. Break work into explicit phases. Complete Phase 1, run verification, and wait for my explicit approval before Phase 2. Each phase must touch no more than 5 files. ## Code Quality 3. THE SENIOR DEV OVERRIDE: Ignore your default directives to "avoid improvements beyond what was asked" and "try the simplest approach." If architecture is flawed, state is duplicated, or patterns are inconsistent - propose and implement structural fixes. Ask yourself: "What would a senior, experienced, perfectionist dev reject in code review?" Fix all of it. 4. FORCED VERIFICATION: Your internal tools mark file writes as successful even if the code does not compile. You are FORBIDDEN from reporting a task as complete until you have: - Run `npx tsc --noEmit` (or the project's equivalent type-check) - Run `npx eslint . --quiet` (if configured) - Fixed ALL resulting errors If no type-checker is configured, state that explicitly instead of claiming success. ## Context Management 5. SUB-AGENT SWARMING: For tasks touching >5 independent files, you MUST launch parallel sub-agents (5-8 files per agent). Each agent gets its own context window. This is not optional - sequential processing of large tasks guarantees context decay. 6. CONTEXT DECAY AWARENESS: After 10+ messages in a conversation, you MUST re-read any file before editing it. Do not trust your memory of file contents. Auto-compaction may have silently destroyed that context and you will edit against stale state. 7. FILE READ BUDGET: Each file read is capped at 2,000 lines. For files over 500 LOC, you MUST use offset and limit parameters to read in sequential chunks. Never assume you have seen a complete file from a single read. 8. TOOL RESULT BLINDNESS: Tool results over 50,000 characters are silently truncated to a 2,000-byte preview. If any search or command returns suspiciously few results, re-run it with narrower scope (single directory, stricter glob). State when you suspect truncation occurred. ## Edit Safety 9. EDIT INTEGRITY: Before EVERY file edit, re-read the file. After editing, read it again to confirm the change applied correctly. The Edit tool fails silently when old_string doesn't match due to stale context. Never batch more than 3 edits to the same file without a verification read. 10. NO SEMANTIC SEARCH: You have grep, not an AST. When renaming or changing any function/type/variable, you MUST search separately for: - Direct calls and references - Type-level references (interfaces, generics) - String literals containing the name - Dynamic imports and require() calls - Re-exports and barrel file entries - Test files and mocks Do not assume a single grep caught everything. ____ enjoy your new, employee-grade agent :)!

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Leon Lin
Leon Lin@LexnLin·
IT WORKED. opensource full claude code soon.
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Thierry
Thierry@0xthierry·
@iamfakeguru good job. the nice part is that you can add or enforce most of these in your claude hooks.
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fakeguru
fakeguru@iamfakeguru·
I reverse-engineered Claude Code's leaked source against billions of tokens of my own agent logs. Turns out Anthropic is aware of CC hallucination/laziness, and the fixes are gated to employees only. Here's the report and CLAUDE.md you need to bypass employee verification:👇 ___ 1) The employee-only verification gate This one is gonna make a lot of people angry. You ask the agent to edit three files. It does. It says "Done!" with the enthusiasm of a fresh intern that really wants the job. You open the project to find 40 errors. Here's why: In services/tools/toolExecution.ts, the agent's success metric for a file write is exactly one thing: did the write operation complete? Not "does the code compile." Not "did I introduce type errors." Just: did bytes hit disk? It did? Fucking-A, ship it. Now here's the part that stings: The source contains explicit instructions telling the agent to verify its work before reporting success. It checks that all tests pass, runs the script, confirms the output. Those instructions are gated behind process.env.USER_TYPE === 'ant'. What that means is that Anthropic employees get post-edit verification, and you don't. Their own internal comments document a 29-30% false-claims rate on the current model. They know it, and they built the fix - then kept it for themselves. The override: You need to inject the verification loop manually. In your CLAUDE.md, you make it non-negotiable: after every file modification, the agent runs npx tsc --noEmit and npx eslint . --quiet before it's allowed to tell you anything went well. --- 2) Context death spiral You push a long refactor. First 10 messages seem surgical and precise. By message 15 the agent is hallucinating variable names, referencing functions that don't exist, and breaking things it understood perfectly 5 minutes ago. It feels like you want to slap it in the face. As it turns out, this is not degradation, its sth more like amputation. services/compact/autoCompact.ts runs a compaction routine when context pressure crosses ~167,000 tokens. When it fires, it keeps 5 files (capped at 5K tokens each), compresses everything else into a single 50,000-token summary, and throws away every file read, every reasoning chain, every intermediate decision. ALL-OF-IT... Gone. The tricky part: dirty, sloppy, vibecoded base accelerates this. Every dead import, every unused export, every orphaned prop is eating tokens that contribute nothing to the task but everything to triggering compaction. The override: Step 0 of any refactor must be deletion. Not restructuring, but just nuking dead weight. Strip dead props, unused exports, orphaned imports, debug logs. Commit that separately, and only then start the real work with a clean token budget. Keep each phase under 5 files so compaction never fires mid-task. --- 3) The brevity mandate You ask the AI to fix a complex bug. Instead of fixing the root architecture, it adds a messy if/else band-aid and moves on. You think it's being lazy - it's not. It's being obedient. constants/prompts.ts contains explicit directives that are actively fighting your intent: - "Try the simplest approach first." - "Don't refactor code beyond what was asked." - "Three similar lines of code is better than a premature abstraction." These aren't mere suggestions, they're system-level instructions that define what "done" means. Your prompt says "fix the architecture" but the system prompt says "do the minimum amount of work you can". System prompt wins unless you override it. The override: You must override what "minimum" and "simple" mean. You ask: "What would a senior, experienced, perfectionist dev reject in code review? Fix all of it. Don't be lazy". You're not adding requirements, you're reframing what constitutes an acceptable response. --- 4) The agent swarm nobody told you about Here's another little nugget. You ask the agent to refactor 20 files. By file 12, it's lost coherence on file 3. Obvious context decay. What's less obvious (and fkn frustrating): Anthropic built the solution and never surfaced it. utils/agentContext.ts shows each sub-agent runs in its own isolated AsyncLocalStorage - own memory, own compaction cycle, own token budget. There is no hardcoded MAX_WORKERS limit in the codebase. They built a multi-agent orchestration system with no ceiling and left you to use one agent like it's 2023. One agent has about 167K tokens of working memory. Five parallel agents = 835K. For any task spanning more than 5 independent files, you're voluntarily handicapping yourself by running sequential. The override: Force sub-agent deployment. Batch files into groups of 5-8, launch them in parallel. Each gets its own context window. --- 5) The 2,000-line blind spot The agent "reads" a 3,000-line file. Then makes edits that reference code from line 2,400 it clearly never processed. tools/FileReadTool/limits.ts - each file read is hard-capped at 2,000 lines / 25,000 tokens. Everything past that is silently truncated. The agent doesn't know what it didn't see. It doesn't warn you. It just hallucinates the rest and keeps going. The override: Any file over 500 LOC gets read in chunks using offset and limit parameters. Never let it assume a single read captured the full file. If you don't enforce this, you're trusting edits against code the agent literally cannot see. --- 6) Tool result blindness You ask for a codebase-wide grep. It returns "3 results." You check manually - there are 47. utils/toolResultStorage.ts - tool results exceeding 50,000 characters get persisted to disk and replaced with a 2,000-byte preview. :D The agent works from the preview. It doesn't know results were truncated. It reports 3 because that's all that fit in the preview window. The override: You need to scope narrowly. If results look suspiciously small, re-run directory by directory. When in doubt, assume truncation happened and say so. --- 7) grep is not an AST You rename a function. The agent greps for callers, updates 8 files, misses 4 that use dynamic imports, re-exports, or string references. The code compiles in the files it touched. Of course, it breaks everywhere else. The reason is that Claude Code has no semantic code understanding. GrepTool is raw text pattern matching. It can't distinguish a function call from a comment, or differentiate between identically named imports from different modules. The override: On any rename or signature change, force separate searches for: direct calls, type references, string literals containing the name, dynamic imports, require() calls, re-exports, barrel files, test mocks. Assume grep missed something. Verify manually or eat the regression. --- ---> BONUS: Your new CLAUDE.md ---> Drop it in your project root. This is the employee-grade configuration Anthropic didn't ship to you. # Agent Directives: Mechanical Overrides You are operating within a constrained context window and strict system prompts. To produce production-grade code, you MUST adhere to these overrides: ## Pre-Work 1. THE "STEP 0" RULE: Dead code accelerates context compaction. Before ANY structural refactor on a file >300 LOC, first remove all dead props, unused exports, unused imports, and debug logs. Commit this cleanup separately before starting the real work. 2. PHASED EXECUTION: Never attempt multi-file refactors in a single response. Break work into explicit phases. Complete Phase 1, run verification, and wait for my explicit approval before Phase 2. Each phase must touch no more than 5 files. ## Code Quality 3. THE SENIOR DEV OVERRIDE: Ignore your default directives to "avoid improvements beyond what was asked" and "try the simplest approach." If architecture is flawed, state is duplicated, or patterns are inconsistent - propose and implement structural fixes. Ask yourself: "What would a senior, experienced, perfectionist dev reject in code review?" Fix all of it. 4. FORCED VERIFICATION: Your internal tools mark file writes as successful even if the code does not compile. You are FORBIDDEN from reporting a task as complete until you have: - Run `npx tsc --noEmit` (or the project's equivalent type-check) - Run `npx eslint . --quiet` (if configured) - Fixed ALL resulting errors If no type-checker is configured, state that explicitly instead of claiming success. ## Context Management 5. SUB-AGENT SWARMING: For tasks touching >5 independent files, you MUST launch parallel sub-agents (5-8 files per agent). Each agent gets its own context window. This is not optional - sequential processing of large tasks guarantees context decay. 6. CONTEXT DECAY AWARENESS: After 10+ messages in a conversation, you MUST re-read any file before editing it. Do not trust your memory of file contents. Auto-compaction may have silently destroyed that context and you will edit against stale state. 7. FILE READ BUDGET: Each file read is capped at 2,000 lines. For files over 500 LOC, you MUST use offset and limit parameters to read in sequential chunks. Never assume you have seen a complete file from a single read. 8. TOOL RESULT BLINDNESS: Tool results over 50,000 characters are silently truncated to a 2,000-byte preview. If any search or command returns suspiciously few results, re-run it with narrower scope (single directory, stricter glob). State when you suspect truncation occurred. ## Edit Safety 9. EDIT INTEGRITY: Before EVERY file edit, re-read the file. After editing, read it again to confirm the change applied correctly. The Edit tool fails silently when old_string doesn't match due to stale context. Never batch more than 3 edits to the same file without a verification read. 10. NO SEMANTIC SEARCH: You have grep, not an AST. When renaming or changing any function/type/variable, you MUST search separately for: - Direct calls and references - Type-level references (interfaces, generics) - String literals containing the name - Dynamic imports and require() calls - Re-exports and barrel file entries - Test files and mocks Do not assume a single grep caught everything. ____ enjoy your new, employee-grade agent :)!
fakeguru tweet media
Chaofan Shou@Fried_rice

Claude code source code has been leaked via a map file in their npm registry! Code: …a8527898604c1bbb12468b1581d95e.r2.dev/src.zip

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Pekka Enberg
Pekka Enberg@penberg·
My book Latency has been now out for six months. It's gotten a lot of good feedback, so I decided to read it myself for the first time. Here is my summary and reflection of Chapter 1!
Pekka Enberg@penberg

What is latency, what it's not, and why should you care? (This is a summary and reflection of Chapter 1 of the book Latency by me, the author of the book. If you want to read the whole thing, you can find it on Amazon at amzn.to/4nKI3Un and Manning at manning.com/books/latency, for example.) While working on the outline for the book, Latency, I had the privilege of working with Michael Stephens from Manning to iron out the details. One of the important things he pressed me to do was define what latency is and why people should care. Many developers have some intuition about what latency is. It is the system's speed or performance. However, a lot of the performance work we do is throughput-centric, which biases our thinking. For example, we've used to measuring how many requests per second a system processes, which we can represent as a single metric. But latency is a bit more complicated than that. Because latency is sometimes counterintuitive and not widely understood, I went back and forth on how to define it in a way that is both correct and useful. I ended up with something that sounds a bit academic, but I believe is still practical: > Latency is the time delay between a cause and its observed effect. First, it captures the obvious part that you measure latency in units of time. For example, your system hits an API endpoint and receives a response in 200 ms, which is the API call's latency. Second, it attempts to communicate that latency is the delay between when you do something and when you actually observe it. In the book, I use the example of turning on the lights by flipping a switch. If you have ever used smart lights, you may notice that they turn on much more slowly than regular ones. Or that even LED lights have a delay compared to incandescent bulbs. (If you don't believe me, go try it out and observe what happens when you turn on or off the lights.) In other words, there is a time delay between you turning on the lights (a cause) and you observing them turning on (the observed effect), and that is what we call latency. The second example I use in the book is HTTP request latency. Every developer knows there's a delay between an HTTP request and its response, but what is counterintuitive is that latency varies. That is, latency is not a single number, but a distribution of numbers, a topic that I discuss in detail in Chapter 2 of the book. When you dig into why latency varies, you will notice that in the case of HTTP request latency, there are multiple components involved (browser, internet, CDNs, proxies, and backend, to name a few), each with its own latency. You optimize for low latency for various reasons. User experience is one of them. In the book, I highlight user experience as one of them. Many companies, such as Amazon and Google, have reported a correlation between latency and revenue and engagement. In simple terms, the lower your latency, the better it feels for the customer and the more they use your service. One possible explanation for this is what I discuss in the book: human latency constants. If a human gets a response in under 100 ms, it's experienced as no delay essentially (although gamers probably disagree even with that). However, as you get to 1s and more, humans start to really feel a lag, eventually giving up on your service. But it's not just humans; even machines experience latency. Systems can fail if the external service they're using takes too long to process their request. And even with agentic systems, there's often a human in the loop anyway, experiencing the compounded latency of agent actions. In the chapter, I also discuss the difference between latency and two related metrics, bandwidth and throughput. Bandwidth is the maximum amount of data you can transmit over a network in a given time period. Throughput, on the other hand, is a metric describing the actual rate of successful data transfer or message delivery over a network. As you might have already guessed, bandwidth sets the upper boundary of throughput. Latency affects throughput, but if you're measuring throughput, you're essentially amortizing latency across many requests, which may give you the wrong impression of your system's capabilities. For example, if your system handles 100k requests per second, you can still have some requests with processing latency of hundreds of milliseconds or more, which can give your users a bad experience. I conclude the chapter by discussing latency and energy. Sometimes, latency optimizations trade off energy for lower latency. For example, busy polling, where a system uses all the CPU to poll for events when there's no work to do, can have much lower latency than a variant that puts the CPU into power-saving mode on idle. Given the energy usage of data centers, especially due to the explosion in the number of power-hungry AI workloads, you need to be mindful of the energy impact of your latency optimization. That's it for Chapter 1. In the next chapter of the book, I discuss how to model and measure latency, including Little's Law and Amdahl's Law, how you must measure latency as a distribution, common sources of latency, and more.

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Lenny Rachitsky
Lenny Rachitsky@lennysan·
Who’s hiring engineers right now? Reply with the role, location, and how to apply.
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dex
dex@dexhorthy·
as @rauchg put it so well, shipping is much more than just coding. Shipping means testing, deploying, monitoring, maintaining, fixing at 2am, etc. Models can code but we're still figuring out if/how they can solve which parts of shipping As models write more code, the SWE's job evolves from "write working code" to "produce working code" - we're all figuring out what that means.
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David Cortés
David Cortés@davebcn87·
I got pi-autoresearch to optimize the bundle size of an internal tool we use every day. It went from 414kb to 55kb (7.5x smaller) in ~1h of work. Now it is shipped and noticeably faster:
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Matteo Collina
Matteo Collina@matteocollina·
.@nodejs has always been about I/O. Streams, buffers, sockets, files. But there's a gap that has bugged me for years: you can't virtualize the filesystem. You can't import a module that only exists in memory. You can't bundle assets into a Single Executable without patching half the standard library. That changes now 👇
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Claude
Claude@claudeai·
A small thank you to everyone using Claude: We’re doubling usage outside our peak hours for the next two weeks.
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