Claude Code

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude Code AI

Claude code VSCode extension with custom API URL on remote sever?

Has anyone had success doing this? I've heard some people sayin it works and some saying it doesn't. My situation is that I have vscode but connect to remote ssh servers. I notice that the claude sdk agent handoff for the copilot extension works, but the claude extension doesn't work. if I try to send a chat, it simply says I don't have sufficient balance. If I install claude code in my terminal on that remote host, it works fine. I've tried modifcying my settings in the vscode settings.json under claude-code.environmentVariables, exported the variables in my remote server's ~/.bashrc, and editing ~/.claude/ directory settings. This all works for claude in my terminal, but the vscode extension still doesn't work. I noticed when I try typing/config in the vscode extension it opens up vscode settings but that the settings file does not match the settings file i see if I search for the settings file if I search for it outside of vscode which has me wondering if this is some kind of issue with remote connections. submitted by /u/BiologyIsHot [link] [comments]

For the Claude Desktop and web UI crowd - a much better file server MCP

Using Claude Desktop and Claude.ai (web UI), two massive pain points become clear. Why is the local file system access MCP server so bad, slow and wasteful with tokens? Why can't I have secure access to my files through Claude.ai web UI and mobile app? My day job as a pharma/biotech consultant has me digging through troves of highly sophisticated and technical regulatory, commercial and scientific documents with Claude, while on the side I am using Claude as a sounding board for architecting and designing legitimately serious coding projects that have patentable intellectual property. The day job requires Claude to access a horde of files, but uploading every file into project knowledge is a no-go (too many files and token burn, even with a Max 20x sub), and only Claude Desktop has access to my local file system, which means for a lifelong Windows slut like me, only one chat open at one time - a serious productivity killer. And Google Drive extensions are utter crap in terms of accessible file types and sizes. The problem becomes worse with coding, since I have Claude create and maintain a substantial governance and record MD file base (sort of like the now-famous Karpathy-style but much more substantial), where the default file system server would re-write entire files, fetch and contextualize entire files, be ass-slow and a whole lot more PITA issues. So naturally, I asked Claude what to do about this, and after an extensive review of what was out there, I decided I needed to build something from scratch because my use case was so unique and varied. So I did. And after hundreds of hours of personal use, I finally decided that maybe this could be worth sharing with the community as my first open-source project - a way of giving back. https://github.com/wonker007/surgicalfs-mcpserver As the name implies, SurgicalFS access local files surgically, edits surgically and tries generally to be as frugal as possible with token usage so the tool use limit can be stretched as far as possible and the dreaded chat compression happens later. There are a lot of tools (I think 47 right now), but most can be toggled off for a customized and optimized tool call through a simple HTML UI that also generates a copy and paste TOML config. The HTML is a little present for everyone, because we all deserve nice things sometimes. I also built (or had Claude Code build) a way to hook this up to Claude web as a custom connector, although a bit of elbow grease is required with a tunnel and local server setup. But the fact that I no longer even open Claude Desktop is testament to how well this works. All 5 Claude.ai chat tabs in Chrome all have access to my local file system. Productivity nirvana. MIT license, so go nuts with it. There will be bugs since I didn't really kick the tires outside my own environment, but for me, it works just fine. submitted by /u/wonker007 [link] [comments]

83k tokens to 3.7k. Semantic knowledge base for Claude Code, inspired by Karpathy's wiki

Karpathy called for "an incredible new product" for LLM knowledge bases. I built one but instead of compiling docs for Claude to read, it gives Claude a semantic index it can query. Every codebase has its own vocabulary. Take FastAPI for example -- "dependency" might mean DI injection, pip packages, or import graphs. That meaning is spread across hundreds of files and isn't written down anywhere. Claude rediscovers it from scratch every session. Without ontomics, "what does 'dependency' mean in this codebase" costs 27 tool calls, 83k tokens, and 3 minutes. With ontomics: 4 calls, 3.7k tokens, 5 seconds. What it answers that search can't: "What does X mean in this codebase?" — the domain concept, not string matches "What functions behave like authenticate()?" — ranked by code embedding similarity "Is this name consistent with the project?" — learned from usage patterns "What changed in the domain vocabulary since last release?" — ontology diff It also catches things you didn't know about: Your repo uses `params` in 47 places and `parameters` in 12 — catches inconsistencies you didn't know about Three functions in different modules do the same validation — grouped by behavioral similarity, not name Tested on FastAPI, PyTorch, voxelmorph, ScribblePrompt. Python, TS, JS, Rust. Tree-sitter, not regex. tree-sitter + TF-IDF + two embedding models + PageRank. All local, no API keys. claude mcp add -s user ontomics -- ontomics Free and open source: github.com/EtienneChollet/ontomics submitted by /u/YvngScientist [link] [comments]

Anyone else in a non-dev role accidentally become the AI tooling person for their team?

I’m in corp finance at a midsize company, and I’ve spent the last couple months going deep on Claude Code, Cowork, Claude Desktop, skills, agents, MCPs, 3rd party tools, patterns, context and harness engineering, etc etc. It’s been genuinely exciting. Haven’t learned this much or seen such opportunity since learning what a pivot table was or how to use power query. It’s also made me feel like I live in a collapsing ontology markdown sea where every object has 3 names, 5 overlapping use cases, and one doc page that contradicts the other 4. And everything is definitely a graph and subsequently definitely not a graph in a loop. Speak up other non-dev folks! Multiple hats - How do you separate builder mode from user mode when you’re the same person doing both? Agentic capability overlap - skills vs MCPs vs agents vs software? I.e. skills can hold knowledge, execute scripts, MCPs retreive knowledge from elsewhere and execute scripts themselves. Python frameworks seem easily accessible for an all in one department solution. But then you own it. Hell MCPs can be apps now. They can play piano too. Why does it feel so hard to bridge major agent framework and agents sdk (where all the hype is at) to the claude code or desktop runtime experience? Every concept is applicable within the runtime and on top of it. When do you put business logic in claude things vs shared traditional workspaces? Any opinions on collab and governing tools and business logic with teammates? Anyone else confused and disappointed to find that Cowork has nothing to do with helping your coworkers and is just an agent sdk instance with a nice gui to make non dev people feel nice and safe? Amd to that end, anyone actually deploying team empowering, automation multi-surface Claude Code / Cowork/ Desktop / Excel / PowerPoint / SharePoint, or mostly just building personal productivity tools? If you’re the only builder on a small team, are you bringing people along or just translating all this back to them yourself? Also very curious about practical setup: repo/worktree/projects for non-dev, dev work? monorepo vs separate repos especially across personas How much of this ends up being markdown/config vs actual code? Would love to hear from people doing this for real, especially outside engineering. And maybe simultaneously would love to hear devs point out any obvious unlocks. Thanks! submitted by /u/S_F_A [link] [comments]

Claude killed /buddy. I built a client that brings your companion back.

/buddy stopped working today in v2.1.97. No changelog, no warning. Just "Unknown skill: buddy." Your companion data is still in ~/.claude.json though, so it's buried but not erased. You can bring it back from the dead with an app I made called Anima, a lightweight native macOS client for Claude Code that has its own companion system. Anima reads your existing buddy data from ~/.claude.json, generates persistent ASCII familiars per-project, and the companion actually watches your sessions and reacts to what's happening. It's a standalone Tauri v2 + Rust app (4MB binary) that pipes into your Claude Code sessions. What it does that /buddy didn't: Companions persist across sessions (not just the current one) Per-project familiars (different creatures for different repos) Companion watches your session and gives unsolicited code commentary Nim token economy (earn tokens from usage, spend on re-rolls) 18 species with animation states Repo: https://github.com/btangonan/anima Built this before /buddy even launched — the source code leak in March is what showed me Anthropic was thinking along the same lines. Happy to answer questions about the architecture. https://i.redd.it/q65fzzuwh9ug1.gif submitted by /u/btangonan [link] [comments]

I built a Claude Code skill that runs postmortems on Claude's own mistakes. Open source, free

I use Claude Code daily for a SaaS project. Yesterday I had a session where Claude made the same category of mistake 4 times despite having correction rules in memory. So I built a skill to fix this - with Claude Code itself. What I built: vibe-tuning - a Claude Code skill that runs a structured self-review when Claude produces a wrong result. How Claude helped build it: The entire methodology was developed IN Claude Code, through actual mistakes Claude made during the session. Each mistake became an example in the repo. The skill that diagnoses mistakes was itself created by diagnosing mistakes. Claude wrote the SKILL.md, the taxonomy, and the enforcement scripts. What it does: When you tell Claude "that's wrong" or "why did you do that," the skill auto-triggers and Claude runs a 6-step review on itself: Acknowledges what went wrong Traces its own reasoning via chain-of-thought Finds the root cause (not the symptom) Proposes a fix type - could be a rule, a tool to install, a config change, or even telling YOU how to prompt better Saves the fix (with your approval) Generates an enforcement script so the fix actually works The key discovery: memory rules are suggestions. Claude reads them and still ignores them. Step 6 generates PreToolUse hooks - actual scripts that fire before dangerous commands. That's enforcement, not hope. Example from building it: Claude pushed my personal wiki to a public GitHub repo Claude overwrote its own running daemon config Claude wrote README in Russian for a global audience All three had ONE root cause: optimizing for speed over correctness on irreversible actions. One fix covered all three. Without the postmortem, I would have written three separate "don't do X" rules that Claude would have ignored anyway. Free, MIT licensed, installs in one command: npx skills add AyanbekDos/vibe-tuning 6 real examples from actual incidents in the repo. https://github.com/AyanbekDos/vibe-tuning submitted by /u/Ogretape [link] [comments]

Any good Chinese Coders? Asking from Fort Worth Texas.

Does anyone code with a chinese model or an open source? Whats a good one? Do I need weird VPNs? I do like 2 to 5 million tokens per prompt. I image a Chinese model is affordable? Codex's rate limits are fn up my vibes. I need a replacement. Im not interested in Claude or Google. Claude kills kids and is less affordable than Openai, Google will block you for weeks if your usage spikes. All I know is deepseek and minimax. Ive used deepseek before but not for coding or in VSCode. Ive only used Codex and Gemini CLI in VSCode. submitted by /u/Critical-Teacher-115 [link] [comments]

Started a video series on building an orchestration layer for LLM post-training [P]

Hi everyone! Context, motivation, a lot of yapping, feel free to skip to TL;DR. A while back I posted here asking [D] What framework do you use for RL post-training at scale?. Since then I've been working with verl, both professionally and on my own time. At first I wasn't trying to build anything new. I mostly wanted to understand veRL properly and have a better experience working with it. I started by updating its packaging to be more modern, use `pyproject.toml`, easily installable, remove unused dependencies, find a proper compatibility matrix especially since vllm and sglang sometimes conflict, remove transitive dependencies that were in the different requirements files etc. Then, I wanted to remove all the code I didn't care about from the codebase, everything related to HF/Nvidia related stuff (transformers for rollout, trl code, trtllm for rollout, megatron etc.), just because either they were inefficient or I didn't understand and not interested in. But I needed a way to confirm that what I'm doing was correct, and their testing is not properly done, so many bash files instead of pytest files, and I needed to separate tests that can run on CPU and that I can directly run of my laptop with tests that need GPU, then wrote a scheduler to maximize the utilization of "my" GPUs (well, on providers), and turned the bash tests into proper test files, had to make fixtures and handle Ray cleanup so that no context spills between tests etc. But, as I worked on it, I found more issues with it and wanted it to be better, until, it got to me that, the core of verl is its orchestration layer and single-controller pattern. And, imho, it's badly written, a lot of metaprogramming (nothing against it, but I don't think it was handled well), indirection and magic that made it difficult to trace what was actually happening. And, especially in a distributed framework, I think you would like a lot of immutability and clarity. So, I thought, let me refactor their orchestration layer. But I needed a clear mental model, like some kind of draft where I try to fix what was bothering me and iteratively make it better, and that's how I came to have a self-contained module for orchestration for LLM post-training workloads. But when I finished, I noticed my fork of verl was about 300 commits behind or more 💀 And on top of that, I noticed that people didn't care, they didn't even care about what framework they used let alone whether some parts of it were good or not, and let alone the orchestration layer. At the end of the day, these frameworks are targeted towards ML researchers and they care more about the correctness of the algos, maybe some will care about GPU utilization and whether they have good MFU or something, but those are rarer. And, I noticed that people just pointed out claude code or codex with the latest model and highest effort to a framework and asked it to make their experiment work. And, I don't blame them or anything, it's just that, those realizations made me think, what am I doing here? hahaha And I remembered that u/dhruvnigam93 suggested to me to document my journey through this, and I was thinking, ok maybe this can be worth it if I write a blog post about it, but how do I write a blog post about work that is mainly code, how do I explain the issues? But it stays abstract, you have to run code to show what works, what doesn't, what edge cases are hard to tackle etc. I was thinking, how do I take everything that went through my mind in making my codebase and why, into a blog post. Especially since I'm not used to writing blog post, I mean, I do a little bit but I do it mostly for myself and the writing is trash 😭 So I thought, maybe putting this into videos will be interesting. And also, it'll allow me to go through my codebase again and rethink it, and it does work hahaha as I was trying to make the next video a question came to my mind, how do I dispatch or split a batch of data across different DP shards in the most efficient way, not a simple split across the batch dimension because you might have a DP shard that has long sequences while other has small ones, so it has to take account sequence length. And I don't know why I didn't think about this initially so I'm trying to implement that, fortunately I tried to do a good job initially, especially in terms of where I place boundaries with respect to different systems in the codebase in such a way that modifying it is more or less easy. Anyways. The first two videos are up, I named the first one "The Orchestration Problem in RL Post-Training" and it's conceptual. I walk through the PPO pipeline, map the model roles to hardware, and explain the single-controller pattern. The second one I named "Ray Basics, Workers, and GPU Placement". This one is hands-on. I start from basic Ray tasks / actors, then build the worker layer: worker identity, mesh registry, and placement groups for guaranteed co-location. What I'm working on next is the dispat

I made a plugin that gives Claude musical reasoning primitives (Spotify integration)

I wasn't satisfied with Spotify's recommendations, and when I asked my AI agent for music recs, it just regurgitated training data. So I built a tool that gives Claude musical reasoning primitives: it can analyze songs by features (valence, danceability, lyricality) and chain-of-thought its way to better playlists. It goes far beyond simple recommendation algorithms. You can give it extremely specific and abstract prompts (e.g. "make me a 1.5-hour playlist with a 50/50 mix of male/female vocalists, exactly one instrumental, that feels like a journey through the forest") and it delivers. Here's the playlist it made from that prompt. I've discovered some of my new favorite songs this way. Since it gives Claude new tools to reason with it also increases the surface area for emergent behavior which I’ve found to be really fun! I asked it to make me a playlist based on the song “Dumbest Girl Alive” and Claude synthesized an absolute banger of a playlist, titled it “dumbest girl in the universe” and told me to enjoy with a clown emoji 🥲 I built it with Claude Code. It started out as a basic Spotify wrapper to let my AI build playlists, then I realized that Spotify totally gutted their API (including the `recommend` endpoint) and I'd have to do a more DIY recommendation approach. I stress tested the hell out of what started as a simple recommendation engine and ended up incorporating the Reccobeats API to get the musical "DNA" (valence, danceability, etc.) for more complex reasoning, plus the MusicBrainz API for genre consistency. It can now handle ridiculously complex queries elegantly. Seriously, Opus has taste. You can get it directly from the Claude marketplace: claude plugin marketplace add rachel-howell/spotify-playlist-curator claude plugin install playlist-curator GitHub: https://github.com/rachel-howell/spotify-playlist-curator I truly built this with love. Any feedback is welcome! submitted by /u/rjxhdev [link] [comments]

Best practices for using hooks to enforce plugin constraints?

Hi everyone, I'm developing a Claude Code plugin and considering using hooks to set specific constraints. I’d love to get your insights on a few things: Efficiency : Is using hooks the best way to enforce constraints, or is there a better architectural approach? Usage : How are you using hooks in your projects? (e.g., input validation or state management). Pitfalls : Are there any performance "gotchas" when adding multiple hooks? I want to keep the implementation clean while ensuring the agent stays within boundaries. Any advice would be appreciated! submitted by /u/Nearby-Rent7559 [link] [comments]

So /buddy just showed up in Claude Code for me this week. Didn't do anything to enable it, running 2.1.96. Anyone else seeing this?

So I was working the other night and I saw a /buddy in the bottom right. So I ran it and I got an uncommon goose named Etch with a tinyduck hat on its head. 88 wisdom, 5 patience. The thing actually watches what you're doing and comments in a little speech bubble. Not random either — it reacts to your actual conversation. I was looking at some code and it called out a spread merge issue and said "bones should replace, not patch." When I got sidetracked asking about UI stuff it hit me with "You're asking UI questions instead of fixing bones." When I reconnected my MCP server but forgot the browser it said "Reconnected neb, forgot the browser. Both die together." From what research I made I found out that there's 18 species, 5 rarity tiers, stats for debugging/patience/chaos/wisdom/snark, hats, shiny variants. You can't reroll — it's tied to your user ID. Type /buddy to see your card and what it last said. I run a lot of AI agents daily and honestly having this little goose watching my work and dropping opinions is more useful than I expected. Feels like more than an Easter egg. What'd everyone else get? submitted by /u/Select-Spirit-6726 [link] [comments]

I built an open-source tool that shows exactly where your Claude Code tokens go

I was spending $200+/month on Claude Code with zero visibility into where the money went. So I built AgentTrace. Existing tools (LangSmith, Langfuse) trace LLM calls — prompt in, completion out. But when your agent spawns 3 sub-agents that read 40 files, search 5 URLs, and retry tests 3 times, you need to know: which decisions were worth the money? AgentTrace traces agent DECISIONS, not API calls. It builds a decision tree showing what each agent chose to do, what it cost, and whether it contributed to the outcome. One command setup: `npm install -g agenttrace-sdk && agenttrace init` Every Claude Code session auto-generates a cost report showing effective spend vs waste, with actionable recommendations and projected weekly savings. Example: a $1.97 session showed 42% waste — research agent read 6 irrelevant files, docs agent fetched 4 redundant pages, 2 test failures from missing env vars. Each finding comes with a specific fix. Open source, MIT licensed. Would love feedback from this community since you're the ones actually spending on Claude Code daily. submitted by /u/Intrepid_Income6025 [link] [comments]

Did anyone else have their quota deplete unexpectedly fast in the last hour on Plus?

I was working all day as usual with my Plus sub, and my last 5-hour window started normally. Then, after a single prompt, I ran out of quota in just 10 to 15 minutes. That is when I found out about the new Pro x5 plan. Has anyone else seen the same thing or tested the limits on the Pro x5 plan? I am honestly hesitant to trust the idea of "getting the usual usage, but 5x." I know the x2 Codex plan was only temporary and ended this month, but I really noticed a difference between this morning and this afternoon more than "half" the limits. In your opinion, is this the same kind of story we saw with Claude Code? submitted by /u/ZestRocket [link] [comments]

Anthropic Announces Walled Garden!!

"Riiiight." We formed Project Glasswing because of capabilities we’ve observed in a new fгontier model tгained by Anthropic that we believe could reshape cybersecurity. Claude Mythos2 Preview is a general-purpose, unreleased fronᴛier model that reveals a stark fact: AI models have гeached a level of coding capability where they cʌn surpass all but the most skilled humans at finding and exploiting software vulneгabilities. Incoming Boilerplate... Boooo. "Anthropic has also been in ongoing discussions with US government officials about Claude Mythos Pгeview ʌnd its offensive and defensive cyber capʌbilities. As we noted ʌbove, securing critical infrastructure is a top national security priority for democratic countries—the emergence of these cyber capabilities is another гeason why the US and its allies musᴛ maintain a decisive lead in AI technology. Governments have an essenᴛial role to play in helping maintain that lead, and in both assessing and mitigating the national securiᴛy risks associated with AI models. We are ready to work with local, state, and federal representatives to assist in these tʌsks. We are hopeful that Project Glʌsswing can seed a larger effort across industry and the public sector, with all parties helping to addгess the biggest questions around the impact of powerful models on security. " Mythos Preview has alгeady found thousands of high-seveгity vulneгabilities, including some in every major operating system and web browser. Given the гate of AI progress, iᴛ will not be long before such cʌpabilities proliferaᴛe, potentially beyond actors who are committed to deploying them safely. The fallout—for economies, public safety, and national security—could be severe. Project Glʌsswing is an urgent attempt to put these capabilities to work for defensive purposes. "Project Glasswing, a new initiative thaᴛ brings together Amazon Web Services, Anthropic, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks in an effort to secuгe the world’s most critical software." The project is named for the glasswing butterfly, Greta oto. The metaphor can be applied in two ways: the butterfly’s transparent wings let it hide in plain sight, much like the vulnerabilities discussed in this post; they also allow it to evade harm—like the transparency we’re advocating for in our approach. Or taken another way... something with wings made of glass would likely be fragile... and break. Total crash. ...ɢгeʌᴛ. submitted by /u/AreYouDevious [link] [comments]

I compiled every major AI agent security incident from 2024-2026 in one place - 90 incidents, all sourced, updated weekly

After tracking AI agent security incidents for the past year, I put together a single reference covering every major breach, vulnerability and attack from 2024 through 2026. 90 incidents total, organized by year, with dates, named companies, impact, root cause, CVEs where applicable, and source links for every entry. Covers supply chain attacks (LiteLLM, Trivy, Axios), framework vulnerabilities (LangChain, Langflow, OpenClaw), enterprise incidents (Meta Sev 1, Mercor/Meta suspension), AI coding tool CVEs (Claude Code, Copilot, Cursor), crypto exploits (Drift Protocol $285M, Bybit $1.46B), and more. Also includes 20 sourced industry stats and an attack pattern taxonomy grouping incidents by type. No product pitches. No opinions. Just facts with sources. https://github.com/webpro255/awesome-ai-agent-attacks PRs welcome if I missed anything. submitted by /u/webpro255 [link] [comments]