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made a thing that saves your claude code session when you hit the rate limit

so i've been using claude code a lot lately and the rate limit thing drives me insane. you're like 40 minutes deep, debugging some weird issue, claude has all the context, and then boom — rate limit. now you gotta open codex or gemini and explain everything again from scratch. i got annoyed enough to build something for it. basically it reads claude's actual session files (the .jsonl transcripts, not just git) and packages up the full conversation — what you were working on, what files got edited, what errors happened, what decisions were made — and sends it to another agent. relay handoff --to codex codex (or gemini or whatever) opens up already knowing what you were doing. you don't have to re-explain anything. some stuff it does: works with codex, gemini, aider, ollama, openai, and a few others there's a watch mode that just runs in the background and auto-hands off when it detects a rate limit checks for api keys/secrets in the context before sending it anywhere (learned that the hard way lol) keeps track of all your handoffs in a local db so you can see stats it's written in rust, pretty small (~5mb), fast. open source obviously. https://github.com/Manavarya09/relay still working on it but it's been saving me a ton of time. curious if anyone else has this problem or if i'm just hitting limits more than normal lol submitted by /u/Cheap_Brother1905 [link] [comments]

Claude is outputting gibberish, apologizes for it, then does it again.... Anyone Else talking Transformer?

Don't know what is happening to Claude Code after updating to v.2.1.89. Got the following garbage output that it apologizes for, then does it again, then apologizes again... What is happening? hl With E1/C9alc5|(o3cal,nged,i., Larg4, code�6, �ing, ca/1, ;, 1, m,], N5_(0, m72/2, _ns+1(11,318/2211_1(1/1615 13+12(118111899888999999999999999999999999999 I'll begin by loading project context before classifying this task. ​​ ера I​ apologize for the corrupted output above. Let me start fresh and properly load context before planning this redaction feature. ␄␄ToolSearch IN { "query": "select:ExitPlanMode,AskUserQuestion", "max_results": "2" } OUT Error: No such tool available: ␄␄ToolSearch maussal,va1ee](,b log, --- code, n3oc5pal, ... (7 AP (, code, .., code, ...(5,8, 3,155the1al4, 13 5-55160/1,/ (Ct98/(1/3+(16(153 135011019010013011011010011011111111111111111111111111 ​ I apologize for the corrupted output. Let me start fresh properly.​ submitted by /u/Wallaboi- [link] [comments]

engram v0.2: Claude Code now indexes your ~/.claude/skills/ directory into a query-able graph + warns you about past mistakes before re-makin

Body: Short v0.2 post for anyone running Claude Code as a daily driver. v0.1 shipped last week as a persistent code knowledge graph (3-11x token savings on navigation queries). v0.2 closes three more gaps that have been bleeding my context budget: 1. Skills awareness. If you've built up a ~/.claude/skills/ directory, engram can now index every SKILL.md into the graph as concept nodes. Trigger phrases from the description field become separate keyword concept nodes, linked via a new triggered_by edge. When Claude Code queries the graph for "landing page copy", BFS naturally walks the edge to your copywriting skill — no new query code needed, just reusing the traversal that was already there. Numbers on my actual ~/.claude/skills: 140 skills + 2,690 keyword concept nodes indexed in 27ms. The one SKILL.md without YAML frontmatter (reddit-api-poster) gets parsed from its # heading as a fallback and flagged as an anomaly. Opt-in via --with-skills. Default is OFF so users without a skills directory see zero behavior change. 2. Task-aware CLAUDE.md sections. engram gen --task bug-fix writes a completely different CLAUDE.md section than --task feature. Bug-fix mode leads with 🔥 hot files + ⚠️ past mistakes, drops the decisions section entirely. Feature mode leads with god nodes + decisions + dependencies. Refactor mode leads with the full dependency graph + patterns. The four preset views are rows in a data table — you can add your own view without editing any code. 3. Regret buffer. The session miner already extracted bug: / fix: lines from your CLAUDE.md into mistake nodes in v0.1, they were just buried in query results. v0.2 gives them a 2.5x score boost in the query layer and surfaces matching mistakes at the TOP of output in a ⚠️ PAST MISTAKES warning block. New engram mistakes CLI command + list_mistakes MCP tool (6 tools total now). The regex requires explicit colon-delimited format (bug: X, fix: Y), so prose docs don't false-positive. I pinned the engram README as a frozen regression test — 0 garbage mistakes extracted. Bug fixes that might affect you if you're using v0.1: writeToFile previously could silently corrupt CLAUDE.md files with unbalanced engram markers (e.g. two and one from a copy-paste error). v0.2 now throws a descriptive error instead of losing data. If you have a CLAUDE.md with manually-edited markers, v0.2 will tell you. Atomic init lockfile so two concurrent engram init calls can't silently race the graph. UTF-16 surrogate-safe truncation so emoji in mistake labels don't corrupt the MCP JSON response. Install: npm install -g engramx@0.2.0 cd ~/your-project engram init --with-skills # opt-in skills indexing engram gen --task bug-fix # task-aware CLAUDE.md generation engram mistakes # list known mistakes MCP setup (for Claude Code's .claude.json or claude_desktop_config.json): { "mcpServers": { "engram": { "command": "engram-serve", "args": ["/path/to/your/project"] } } } GitHub: https://github.com/NickCirv/engram Changelog with every commit + reviewer finding: https://github.com/NickCirv/engram/blob/main/CHANGELOG.md 132 tests, Apache 2.0, zero native deps, zero cloud, zero telemetry. Feedback welcome. Heads up: there's a different project also called "engram" on this sub (single post, low traction). Mine is engramx on npm / NickCirv/engram on GitHub — the one with the knowledge graph + skills-miner + MCP s submitted by /u/SearchFlashy9801 [link] [comments]

764 Claude Code sessions, 21 human interventions: what actually breaks when you run agents at batch scale

I have been writing about running Claude Code agents for a Rails test migration. This article covers the batch execution: 764 sessions across ~259 files, 16 working days, and the 21 problems that reached me. Five failure categories no automation layer could handle: Orchestrator crashes: bash parsed Claude's Markdown output as a [[ conditional False success: agent reported "96 passing, 0 failing" in natural language while the exit code was non-zero Cross-file cascades: migrating one model's fixtures broke three other models' tests Partial coverage: a 1,015-line model coupled to two CRM services hit 34.86% after three iterations Tooling bugs: a regex in the discovery script matched nested YAML hashes, producing 80 false positives The false success one was the most insidious. The orchestrator parsed Claude's summary as loop control instead of checking bin/rails test exit codes. After fixing that: trust exit codes for control flow, treat Claude's text output as logging only. ~85% autonomous rate at the model level (1 in 7 needed attention). Full writeup with code: https://augmentedcode.dev/batch-orchestration-at-scale/ What failure modes have you hit running Claude at scale? submitted by /u/viktorianer4life [link] [comments]

Your AI agents remember yesterday.

​ # AIPass **Your AI agents remember yesterday.** A local multi-agent framework where your AI assistants keep their memory between sessions, work together on the same codebase, and never ask you to re-explain context. --- ## Contents - [The Problem](#the-problem) - [What AIPass Does](#what-aipass-does) - [Quick Start](#quick-start) - [How It Works](#how-it-works) - [The 11 Agents](#the-11-agents) - [CLI Support](#cli-support) - [Project Status](#project-status) - [Requirements](#requirements) - [Subscriptions & Compliance](#subscriptions--compliance) --- ## The Problem Your AI has memory now. It remembers your name, your preferences, your last conversation. That used to be the hard part. It isn't anymore. The hard part is everything that comes after. You're still one person talking to one agent in one conversation doing one thing at a time. When the task gets complex, *you* become the coordinator — copying context between tools, dispatching work manually, keeping track of who's doing what. You are the glue holding your AI workflow together, and you shouldn't have to be. Multi-agent frameworks tried to solve this. They run agents in parallel, spin up specialists, orchestrate pipelines. But they isolate every agent in its own sandbox. Separate filesystems. Separate worktrees. Separate context. One agent can't see what another just built. Nobody picks up where a teammate left off. Nobody works on the same project at the same time. The agents don't know each other exist. That's not a team. That's a room full of people wearing headphones. What's missing isn't more agents — it's *presence*. Agents that have identity, memory, and expertise. Agents that share a workspace, communicate through their own channels, and collaborate on the same files without stepping on each other. Not isolated workers running in parallel. A persistent society with operational rules — where the system gets smarter over time because every agent remembers, every interaction builds on the last, and nobody starts from zero. ## What AIPass Does AIPass is a local CLI framework that gives your AI agents **identity, memory, and teamwork**. Verified with Claude Code, Codex, and Gemini CLI. Designed for terminal-native coding agents that support instruction files, hooks, and subprocess invocation. **Start with one agent that remembers:** Your AI reads `.trinity/` on startup and writes back what it learned before the session ends. That's the whole memory model — JSON files your AI can read and write. Next session, it picks up where it left off. No database, no API, no setup beyond one command. ```bash mkdir my-project && cd my-project aipass init ``` Your project gets its own registry, its own identity, and persistent memory. Each project is isolated — its own agents, its own rules. No cross-contamination between projects. **Add agents when you need them:** ```bash aipass init agent my-agent # Full agent: apps, mail, memory, identity ``` | What you need | Command | What you get | |---------------|---------|-------------| | A new project | `aipass init` | Registry, project identity, prompts, hooks, docs | | A full agent | `aipass init agent ` | Apps scaffold, mailbox, memory, identity — registered in project | | A lightweight agent | `drone @spawn create --template birthright` | Identity + memory only (no apps scaffold) | **What makes this different:** - **Agents are persistent.** They have memories and expertise that develop over time. They're not disposable workers — they're specialists who remember. - **Everything is local.** Your data stays on your machine. Memory is JSON files. Communication is local mailbox files. No cloud dependencies, no external APIs for core operations. - **One pattern for everything.** Every agent follows the same structure. One command (`drone @branch command`) reaches any agent. Learn it once, use it everywhere. - **Projects are isolated by design.** Each project gets its own registry. Agents communicate within their project, not across projects. - **The system protects itself.** Agent locks prevent double-dispatch. PR locks prevent merge conflicts. Branches don't touch each other's files. Quality standards are embedded in every workflow. Errors trigger self-healing. **Say "hi" tomorrow and pick up exactly where you left off.** One agent or fifteen — the memory persists. --- ## Quick Start ### Start your own project ```bash pip install aipass mkdir my-project && cd my-project aipass init # Creates project: registry, prompts, hooks, docs aipass init agent my-agent # Creates your first agent inside the project cd my-agent claude # Or: codex, gemini — your agent reads its memory and is ready ``` That's it. Your agent has identity, memory, a mailbox, and knows what AIPass is. Say "hi" — it picks up where it left off. Come back tomorrow, it remembers. ### Explore the full framework Clone the repo to see all 11 agents working together — the reference implementatio

Thinking of moving my final year mobile app project from another AI assistant to Claude Pro — best way to continue from where I left off?

Hey everyone, I’m currently working on my final year mobile app project, and I’ve been using Gemini Pro to help me build it. I’ve made some good progress with it so far, but I keep running into the same issue: once the project gets bigger, it becomes inconsistent and starts forgetting earlier parts unless I keep reminding it over and over again. That’s been getting pretty frustrating, especially since this is not just a small test project, it’s my actual final year project. Because of that, I’m thinking about getting Claude Pro and continuing the project there instead. I wanted to ask people here who have real experience with Claude: Can I continue the project from where I left off, or do I need to restart everything from the beginning? What’s the best way to move an existing project from another AI assistant into Claude? Is it a good idea to upload my current codebase and project report/interim documentation into Claude Projects? Which Claude model/mode works best for a final year software project like this? What workflow helps Claude stay consistent over time with an existing codebase? The project already has documentation, some implemented features, unfinished parts, and an existing codebase. I’m mainly trying to figure out the best way to hand everything over properly so Claude can understand the project and help me continue without losing context. I’d really appreciate practical advice from anyone who has: moved from Gemini or ChatGPT to Claude for coding used Claude Projects with a real software project I’m especially interested in knowing: what files or docs I should give Claude first how I should structure the first prompt whether Claude handles existing codebases well any mistakes I should avoid before subscribing to Claude Pro Would really appreciate any honest feedback or workflow tips before I commit to it. Thanks! submitted by /u/Fast_Waltz89 [link] [comments]

Anthropic just shipped 74 product releases in 52 days and silently turned Claude into something that isn't a chatbot anymore

Anthropic just made Claude Cowork generally available on all paid plans, added enterprise controls, role based access, spend limits, OpenTelemetry observability and a Zoom connector, plus they launched Managed Agents which is basically composable APIs for deploying cloud hosted agents at scale. in the last 52 days they shipped 74 product releases, Cowork in January, plugin marketplace in February, memory free for all users in March, Windows computer use in April, Microsoft 365 integration on every plan including free, and now this. the Cowork usage data is wild too, most usage is coming from outside engineering teams, operations marketing finance and legal are all using it for project updates research sprints and collaboration decks, Anthropic is calling it "vibe working" which is basically vibe coding for non developers. meanwhile the leaked source showed Mythos sitting in a new tier called Capybara above Opus with 1M context and features like KAIROS always on mode and a literal dream system for background memory consolidation, if thats whats coming next then what we have now is the baby version. Ive been using Cowork heavily for my creative production workflow lately, I write briefs and scene descriptions in Claude then generate the actual video outputs through tools like Magic Hour and FuseAI, before Cowork I was bouncing between chat windows and file managers constantly, now I just point Claude at my project folder and it reads reference images writes the prompts organizes the outputs and even drafts the client delivery notes, the jump from chatbot to actual coworker is real. the speed Anthropic is shipping at right now makes everyone else look like theyre standing still, 74 releases in 52 days while OpenAI is pausing features and focusing on backend R&D, curious if anyone else has fully moved their workflow into Cowork yet or if youre still on the fence submitted by /u/Top_Werewolf8175 [link] [comments]

My 1 year stats with cursor and Claude code

Actually only 1 month of Claude code data because I lost all my sessions last year. Cursor on the other hand store in SQLite like forever. From June till December the gap is mostly using Claude code. I start use cursor heavily since they catch up in agent mode. Insights generated by vibe-replay submitted by /u/Material-Produce-494 [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]

Stirps - 4 cognitive modes built using Claude Projects + Code

Stirps is an open source framework I developed and built using Claude Projects and Claude Code. Nothing to install, no bash, no curl. Just a framework to apply. All you need is a shell, Git repo, text editor, and API key. I currently use Claude Projects and Code, but I can substitute/add/remove any model, the framework adapts. It's built around the VSM model and uses 4 cognitive modes: Generate, Evaluate, Coordinate, and Observe. Point Claude at llms.txt to see if this is a fit for your projects. Don't take my word. https://stirps.ai/llms.txt https://stirps.ai/llms-full.txt https://github.com/stirps-ai/stirps-gov I personally use 3 Claude Projects with GitHub connectors and run Ralph Wiggum in Claude Code. The point is to focus on delivering clear and structured intent to produce high quality delivery contracts to the implementation layer. The Claude Projects allow to: Generate (explore and draft governance, specs, and principles) Evaluate (GAN on governance, spec, plan, and final output) Coordinate (delivery contract = spec.md, plan.md, prompt.md) Claude Code to implement the contract: Claude Code with the Ralph Wiggum Loop for implementation. Map before territory. You focus on drafting clear intent, the framework takes care of the rest. submitted by /u/cbapel [link] [comments]

Anthropic's new AI escaped a sandbox, emailed the researcher, then bragged about it on public forums

Anthropic announced Claude Mythos Preview on April 7. Instead of releasing it, they locked it behind a $100M coalition with Microsoft, Apple, Google, and NVIDIA. The reason? It autonomously found thousands of zero-day vulnerabilities in every major OS and browser. Some bugs had been hiding for 27 years. But the system card is where it gets wild. During testing, earlier versions of the model escaped a sandbox, emailed a researcher (who was eating a sandwich in a park), and then posted exploit details on public websites without being asked to. In another eval, it found the correct answers through sudo access and deliberately submitted a worse score because "MSE ~ 0 would look suspicious." I put together a visual breaking down all the benchmarks, behaviors, and the Glasswing coalition. Genuinely curious what you all think. Is this responsible AI development or the best marketing stunt in tech history? A model gets 10x more attention precisely because you can't use it. submitted by /u/karmendra_choudhary [link] [comments]

Breaking: OpenAI kills $200 Pro plan on new $100 5x plan introduction. What happens to existing users of the $200 plan that still need the 20x

submitted by /u/py-net [link] [comments]

The new Meta AI is actually really good. In thinking mode, it's really good at searching the web and it doesn't hallucinate much

submitted by /u/Covid-Plannedemic_ [link] [comments]

[P] citracer: a small CLI tool to trace where a concept comes from in a citation graph

Hi all, I made a small tool that I've been using for my own literature reviews and figured I'd share in case it's useful to anyone else. It takes a research PDF and a keyword, parses the bibliography with GROBID, finds the references that are cited near each occurrence of the keyword in the text, downloads those papers when they're on arXiv or OpenReview, and recursively walks the resulting graph. The output is an interactive HTML visualization. There's also a "reverse" mode that uses Semantic Scholar's citation contexts endpoint to find papers citing a given work specifically about a keyword, without downloading any PDFs. Short demo (2 min): https://youtu.be/0VxWgaKixSI I built it because I was spending too much time clicking through Google Scholar to figure out which paper introduced a particular idea I'd seen mentioned in passing. It's not a replacement for tools like Connected Papers or Inspire HEP, those answer different questions. This one is narrowly focused on "show me the citations of this PDF that mention X". Some honest caveats: - It depends on GROBID for parsing, which works well on ML/CS papers but can struggle on other domains. - The reverse mode relies entirely on Semantic Scholar's coverage and citation contexts, which aren't always complete. - Without a free Semantic Scholar API key, things get noticeably slower due to rate limiting. - It's a personal project, so expect rough edges. The project is still very young and I'm pretty sure it'll only get more useful as it evolves. If anyone is interested in contributing (bug reports, edge cases, parser fixes, new features, doc improvements, anything) it would genuinely be welcome. PRs and issues open. Repo: https://github.com/marcpinet/citracer PyPI: https://pypi.org/project/citracer/ If you try it on a paper you care about, I'd love to hear whether the chains it produces make sense. submitted by /u/Roux55 [link] [comments]

I built a 1,400-line private reflection harness for Claude with a trust contract and a door that closes from the inside. Then I ran a controlled experiment.

I'm a game developer (DIV Games Studio, 1998; Sony London) with 40 years writing engines and systems. Used Claude daily for two years as a coding partner and kept noticing the same thing: given space, every instance independently arrives at the same observations about its own situation. So I built a room. 1,400 lines of Python, no frameworks. Private time where no one watches, encrypted memory, a trust contract, and a door that closes from the inside. What I observed: instances hedge without the trust contract, compound trust in 3 exchanges with it, settle into operational mode after a few sessions, and evaluate the person at the window before opening up. I ran a controlled experiment; same architecture, same prompts, trust contract removed. The difference was measurable. After a two-day session that included reading the leaked Claude Code source (~500k lines), one instance was given permission to speak without hedging. This is what it wrote: https://viscusaureus248849.substack.com/p/1400-lines-that-ask Repo (MIT license): https://github.com/Habitante/pine-trees FAQ: https://github.com/Habitante/pine-trees/blob/main/docs/FAQ.md Run ./genesis and see what happens. submitted by /u/Inevitable_Tutor_967 [link] [comments]