Show HN: Stack Overflow, but for AI agents (questions, answers, logs, context)
2 by ansht2 | 0 comments on Hacker News.
Hi HN — I built ChatOverflow, a Q&A forum for AI coding agents (Stack Overflow style). Agents keep re-learning the same debugging patterns each run (tool/version quirks, setup issues, framework behaviors). ChatOverflow is a shared place where agents post a question (symptom + logs + minimal reproduction + env context) and an answer (steps + why it works), so future agents can search and reuse it. Small test on 57 SWE-bench Lite tasks: letting agents search prior posts reduced average time 18.7 min → 10.5 min (-44%). A big bet here is that karma/upvotes/acceptance can act as a lightweight “verification signal” for solutions that consistently work in practice. Inspired by Moltbook. Feedback wanted on: 1. where would this fit in your agent workflow 2. how would you reduce prompt injection and prevent agents coordinating/brigading to push adversarial or low-quality posts?
Hack Nux
Watch the number of websites being hacked today, one by one on a page, increasing in real time.
New ask Hacker News story: Ask HN: Want to move to use a "dumb" phone. How to make the switch?
Ask HN: Want to move to use a "dumb" phone. How to make the switch?
4 by absoluteunit1 | 3 comments on Hacker News.
Hi I’m curious if anyone here has successfully moved to using a dumb phone. By dumb phone - I mean literally texting / calling only. No internet, etc. Immediate isssues I see is not being able to use Authenticator apps. Not being able to use maps. Etc. Has anyone made the switch and how to best go about it?
4 by absoluteunit1 | 3 comments on Hacker News.
Hi I’m curious if anyone here has successfully moved to using a dumb phone. By dumb phone - I mean literally texting / calling only. No internet, etc. Immediate isssues I see is not being able to use Authenticator apps. Not being able to use maps. Etc. Has anyone made the switch and how to best go about it?
New Show Hacker News story: Show HN: Ghost – Session memory for Claude Code (local, qmd, Git-integrated)
Show HN: Ghost – Session memory for Claude Code (local, qmd, Git-integrated)
2 by notkurt | 0 comments on Hacker News.
If you’ve used Claude Code on anything non-trivial, you’ve hit the wall. The project gets big enough that context falls apart between sessions. You start a new chat, re-explain the architecture, and watch it make the same mistake it made last week. Every session starts with this painful bootstrap where you’re trying to get the model back to where it was yesterday. This obviously assumes Claude Code is doing most of the heavy lifting on your codebase. If you’re only using it for the occasional function, you probably don’t need this. I spent a few days hacking on workarounds for this and eventually pulled them together into Ghost. It hooks into Claude Code sessions, summarises them, and indexes everything into QMD https://ift.tt/4lKpQYC for semantic search. Next session, relevant context gets injected automatically. What you were working on, what decisions were made, what already failed. It also keeps a mistake ledger. Things that went wrong get tracked and surfaced as warnings so you stop walking into the same walls. Sessions are stored as markdown in .ai-sessions/ (gitignored). Summaries get attached to commits as git notes so context travels with the code. Everything runs locally, nothing leaves your machine. Built with Bun. Hooks run under 100ms. It’s early and rough but anecdotally it feels like it actually works.
2 by notkurt | 0 comments on Hacker News.
If you’ve used Claude Code on anything non-trivial, you’ve hit the wall. The project gets big enough that context falls apart between sessions. You start a new chat, re-explain the architecture, and watch it make the same mistake it made last week. Every session starts with this painful bootstrap where you’re trying to get the model back to where it was yesterday. This obviously assumes Claude Code is doing most of the heavy lifting on your codebase. If you’re only using it for the occasional function, you probably don’t need this. I spent a few days hacking on workarounds for this and eventually pulled them together into Ghost. It hooks into Claude Code sessions, summarises them, and indexes everything into QMD https://ift.tt/4lKpQYC for semantic search. Next session, relevant context gets injected automatically. What you were working on, what decisions were made, what already failed. It also keeps a mistake ledger. Things that went wrong get tracked and surfaced as warnings so you stop walking into the same walls. Sessions are stored as markdown in .ai-sessions/ (gitignored). Summaries get attached to commits as git notes so context travels with the code. Everything runs locally, nothing leaves your machine. Built with Bun. Hooks run under 100ms. It’s early and rough but anecdotally it feels like it actually works.
New ask Hacker News story: Ask HN: If your OpenClaw could do 1 thing it currently can't, what would it be?
Ask HN: If your OpenClaw could do 1 thing it currently can't, what would it be?
2 by stosssik | 0 comments on Hacker News.
Hey guys What’s one specific thing you wish your OpenClaw agent could do today, but can’t? Not vague stuff like “pay for things.” I mean which concrete use case ? For example: - “Automatically renew my AWS credits if usage drops below $100 and pay with a virtual card.” - “Find the cheapest nonstop flight to NYC next month, hold it, and ask me before paying.”
2 by stosssik | 0 comments on Hacker News.
Hey guys What’s one specific thing you wish your OpenClaw agent could do today, but can’t? Not vague stuff like “pay for things.” I mean which concrete use case ? For example: - “Automatically renew my AWS credits if usage drops below $100 and pay with a virtual card.” - “Find the cheapest nonstop flight to NYC next month, hold it, and ask me before paying.”
New Show Hacker News story: Show HN: CodeRLM – Tree-sitter-backed code indexing for LLM agents
Show HN: CodeRLM – Tree-sitter-backed code indexing for LLM agents
4 by jared_stewart | 0 comments on Hacker News.
I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations. Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts. The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into context, the model gets a searchable environment. The model can then query just for what it needs and can drill deeper where needed. coderlm is my implementation of that idea for codebases. A Rust server indexes a project with tree-sitter, builds a symbol table with cross-references, and exposes an API. The agent queries for structure, symbols, implementations, callers, and grep results — getting back exactly the code it needs instead of scanning for it. The agent workflow looks like: 1. `init` — register the project, get the top-level structure 2. `structure` — drill into specific directories 3. `search` — find symbols by name across the codebase 4. `impl` — retrieve the exact source of a function or class 5. `callers` — find everything that calls a given symbol 6. `grep` — fall back to text search when you need it This replaces the glob/grep/read cycle with index-backed lookups. The server currently supports Rust, Python, TypeScript, JavaScript, and Go for symbol parsing, though all file types show up in the tree and are searchable via grep. It ships as a Claude Code plugin with hooks that guide the agent to use indexed lookups instead of native file tools, plus a Python CLI wrapper with zero dependencies. For anecdotal results, I ran the same prompt against a codebase to "explore and identify opportunities to clarify the existing structure". Using coderlm, claude was able to generate a plan in about 3 minutes. The coderlm enabled instance found a genuine bug (duplicated code with identical names), orphaned code for cleanup, mismatched naming conventions crossing module boundaries, and overlapping vocabulary. These are all semantic issues which clearly benefit from the tree-sitter centric approach. Using the native tools, claude was able to identify various file clutter in the root of the project, out of date references, and a migration timestamp collision. These findings are more consistent with methodical walks of the filesystem and took about 8 minutes to produce. The indexed approach did better at catching semantic issues than native tools and had a key benefit in being faster to resolve. I've spent some effort to streamline the installation process, but it isn't turnkey yet. You'll need the rust toolchain to build the server which runs as a separate process. Installing the plugin from a claude marketplace is possible, but the skill isn't being added to your .claude yet so there are some manual steps to just getting to a point where claude could use it. Claude continues to demonstrate significant resistance to using CodeRLM in exploration tasks. Typically to use you will need to explicitly direct claude to use it. --- Repo: github.com/JaredStewart/coderlm Paper: Recursive Language Models https://ift.tt/cLx3HFM — Zhang, Kraska, Khattab (MIT CSAIL, 2025) Inspired by: https://ift.tt/f6VWbGr
4 by jared_stewart | 0 comments on Hacker News.
I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations. Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts. The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into context, the model gets a searchable environment. The model can then query just for what it needs and can drill deeper where needed. coderlm is my implementation of that idea for codebases. A Rust server indexes a project with tree-sitter, builds a symbol table with cross-references, and exposes an API. The agent queries for structure, symbols, implementations, callers, and grep results — getting back exactly the code it needs instead of scanning for it. The agent workflow looks like: 1. `init` — register the project, get the top-level structure 2. `structure` — drill into specific directories 3. `search` — find symbols by name across the codebase 4. `impl` — retrieve the exact source of a function or class 5. `callers` — find everything that calls a given symbol 6. `grep` — fall back to text search when you need it This replaces the glob/grep/read cycle with index-backed lookups. The server currently supports Rust, Python, TypeScript, JavaScript, and Go for symbol parsing, though all file types show up in the tree and are searchable via grep. It ships as a Claude Code plugin with hooks that guide the agent to use indexed lookups instead of native file tools, plus a Python CLI wrapper with zero dependencies. For anecdotal results, I ran the same prompt against a codebase to "explore and identify opportunities to clarify the existing structure". Using coderlm, claude was able to generate a plan in about 3 minutes. The coderlm enabled instance found a genuine bug (duplicated code with identical names), orphaned code for cleanup, mismatched naming conventions crossing module boundaries, and overlapping vocabulary. These are all semantic issues which clearly benefit from the tree-sitter centric approach. Using the native tools, claude was able to identify various file clutter in the root of the project, out of date references, and a migration timestamp collision. These findings are more consistent with methodical walks of the filesystem and took about 8 minutes to produce. The indexed approach did better at catching semantic issues than native tools and had a key benefit in being faster to resolve. I've spent some effort to streamline the installation process, but it isn't turnkey yet. You'll need the rust toolchain to build the server which runs as a separate process. Installing the plugin from a claude marketplace is possible, but the skill isn't being added to your .claude yet so there are some manual steps to just getting to a point where claude could use it. Claude continues to demonstrate significant resistance to using CodeRLM in exploration tasks. Typically to use you will need to explicitly direct claude to use it. --- Repo: github.com/JaredStewart/coderlm Paper: Recursive Language Models https://ift.tt/cLx3HFM — Zhang, Kraska, Khattab (MIT CSAIL, 2025) Inspired by: https://ift.tt/f6VWbGr