Updated August 17, 2026 · Refreshed weekly
Find AI coding tool leads on Reddit
Real developers asking for AI coding tools and workflow fixes on Reddit right now, updated weekly.
Developers don't discover tools from ads - they ask other developers. Subreddits like r/ClaudeAI, r/cursor, r/ChatGPTCoding and r/ExperiencedDevs see daily posts from people hitting real limits: agents losing context, vibe-coded apps that need security review, deploy bottlenecks, token costs, coordinating multiple coding agents.
Every post below was found by Leadverse and filtered for buying intent - meaning the author described a concrete workflow problem an AI dev tool can solve, not just general AI chatter. If you're building for developers, this is your market speaking in its own words, refreshed weekly.
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High-quality leads grouped by buying intent
Exploring Solutions
109Tool Request
90Troubleshooting
65Pain / Frustration
11Comparison
6Alternative Search
2Live now
Live ai coding tools leads from Reddit
Anonymized previews - the full posts, links and outreach drafts are inside Leadverse.
This page is a public preview of Reddit posts where people are asking for AI coding tools and dev workflow products. Leadverse finds these conversations so you can spot potential customers before they go cold.
Tool Request
Vibe coding strategies with Free VS Vode Ai extensions
Hello everbody, i'm algo trader and i have a little few knoweldge and skills in coding, i use python for building trading bots, but i just finishing a backtesting engine with complete vibe coding it tooks me too long around 3 months to finish it, and now i need just a complete free ai to test the strategies bsed on this engine logics, so i need an ai that know all the files and how to deal with them, using vine coding with chat ai is complete hell, the only ai i was using is Claude by bypass him with 6 accounts to be able to finish the full engine, i really will be stuck if i didn't get a free ai for this mission, i finish building heavy scripts, right now just a strategy script and that's it ! so please any ideas ? my pc is pretty low end pc (i5-4th, 8 ram, 500gb HDD)00
OpenTool Request
I need a free AI extension in VS Code for coding and editing files
Hello everbody, i'm algo trader and i have a little few knoweldge and skills in coding, i use python for building trading bots, but i just finishing a backtesting engine with complete vibe coding it tooks me too long around 3 months to finish it, and now i need just a complete free ai to test the strategies bsed on this engine logics, so i need an ai that know all the files and how to deal with them, using vine coding with chat ai is complete hell, the only ai i was using is Claude by bypass him with 6 accounts to be able to finish the full engine, i really will be stuck if i didn't get a free ai for this mission, i finish building heavy scripts, right now just a strategy script and that's it ! so please any ideas ? my pc is pretty low end pc (i5-4th, 8 ram, 500gb HDD)00
OpenTool Request
I can build the agent. What am I supposed to do once I have 10 of them?
I've been someone who started building stuff in last 2 yrs so, no-code AI tools lately, and something has been bugging me. Building and deploying and testing one agent seems textbook now. But then I started wondering what happens when people actually start applying these things seriously. Say I have 20 agents across different workflows: one handles lead qualification, one summarizes support tickets, one works with internal docs, one handles reporting, one triggers automations At that point for real work, what's used to keep track...like How do I know which agents I have? How do I version them when I change prompts/tools? How do I control what each agent is allowed to access? How do I test an agent before letting it loose on real users/data? How do I see what actually happened when an agent makes a bad decision? And if I'm a no-code builder, I'd really rather not have to suddenly learn a whole DevOps stack just to manage the things I created without code 😅 I'm curious how people here handle this today. Are there really any no-code tool capable of this? Is the normal answer basically "use something like n8n/Make/Zapier + spreadsheets + logging + some manual discipline", or are the newer AI-agent platforms starting to solve the management/governance layer as well? I've seen Lyzr's control plane/ Agent studio discussed as one approach to this, while products like Relevance AI, Microsoft Copilot Studio and others are coming at the broader no-code/agent-management problem from different angles. Would be interested to hear what people here are actually using once they go beyond 1–2 agents or what companies or start-ups use, and where the no-code abstraction starts to break down?220
OpenTroubleshooting
I can build the agent. What am I supposed to do once I have 10 of them?
I've been someone who started building stuff in last 2 yrs so, no-code AI tools lately, and something has been bugging me. Building and deploying and testing one agent seems textbook now. But then I started wondering what happens when people actually start applying these things seriously. Say I have 20 agents across different workflows: one handles lead qualification, one summarizes support tickets, one works with internal docs, one handles reporting, one triggers automations At that point for real work, what's used to keep track...like How do I know which agents I have? How do I version them when I change prompts/tools? How do I control what each agent is allowed to access? How do I test an agent before letting it loose on real users/data? How do I see what actually happened when an agent makes a bad decision? And if I'm a no-code builder, I'd really rather not have to suddenly learn a whole DevOps stack just to manage the things I created without code 😅 I'm curious how people here handle this today. Are there really any no-code tool capable of this? Is the normal answer basically "use something like n8n/Make/Zapier + spreadsheets + logging + some manual discipline", or are the newer AI-agent platforms starting to solve the management/governance layer as well? I've seen Lyzr's control plane/ Agent studio discussed as one approach to this, while products like Relevance AI, Microsoft Copilot Studio and others are coming at the broader no-code/agent-management problem from different angles. Would be interested to hear what people here are actually using once they go beyond 1–2 agents or what companies or start-ups use, and where the no-code abstraction starts to break down?01
OpenTool Request
Is it possible ?
I wanted to build a project for my second year of engineering. I am thinking to build self help website. Building and tracking of habits and new feature that will allow user to learn any one topic from particular field he want to learn and many other feature i will add. The problem I don't know much about web development. AI can build templates and working webpages. But I always stuck to the part of backed and deployment. Is it possible to make backed and deployment with AI ?03
OpenExploring Solutions
Are we cooked?
I’m a web developer with 10+ years of experience. Today I generated a genuinely unique, award-worthy website design in ChatGPT, with instructions for it to be exported as a WordPress/Elementor theme. From generating the site to installing it on WordPress and having it actually working took less than an hour. And I’m sitting here thinking… are we cooked? 😂 How much time do you think web developers realistically have before we get a tool where you type: \ “Build me a unique, high-end website for this business.” …and one click later you have the design, responsive pages, CMS, animations, SEO basics, WordPress integration, and everything else ready to go? Not another generic AI template — I mean genuinely custom, polished websites. 1 year? 2 years? Or are we basically already there?031
OpenComparison
are no code app builders actually useful or just hype
Every landing page for these tools promises you'll ship a full product in hours. Skeptical after getting burned by three different platforms last year. Anyone used one long enough to say it actually held up past the first week?06
OpenTool Request
LocalAI to help on my server
I´m not sure if this is the right place to ask, but i wanted to run an AI that could help me on my local server. Like running check-ups backups and all that. I wanted to know if the best way to do it is to set-up something like OpenClaw or Hermes, or OpenWebUI with an MCP server? I have a 3060 12GB running Qwen3.5:9B already. Any advice or help?13
OpenComparison
SQLite-based memory persistence vs cloud memory for coding agents. What is actually working for you?
I have been testing memory setups for coding agents for a few months and I keep going back and forth on local SQLite vs cloud-hosted memory. What I have found so far: Local SQLite pros: everything stays on your machine, you can open the file with sqlite3 and read or fix any memory directly, retrieval is fast because there is no network hop, and there is no per-call API cost. If a memory is wrong you just edit the row. Local SQLite cons: no team sharing out of the box, you own backups yourself. Cloud memory pros: sync across machines, managed infra. Cloud cons: your agent's accumulated knowledge of your codebase lives on someone else's servers, latency on every retrieval, and if you leave the vendor you often cannot take the memory with you in a usable form. For accuracy specifically, the biggest win from local was inspectability. When the agent kept applying a retired convention, I could see the stale entry and delete it. With a hosted black box I never knew what it was retrieving. Full disclosure, I am on the team building Holmes (local-first SQLite memory for coding agents, currently private beta), so I am biased toward local. But I am genuinely curious what setups are working for people here, especially anyone running cloud memory at team scale.10
OpenTool Request
Before I spend months building an AI memory/knowledge system, does a free open-source project like this already exist?
Hey everyone, I’m about to start building something pretty big, but before I waste months reinventing the wheel, I wanted to ask this community: Does a free open-source project already exist that does something like this? If something close already exists, I would much rather use it, contribute to it, and improve it instead of building everything from scratch. What I’m looking for is basically an AI-native knowledge base / memory system for AI agents. The problem I’m trying to solve: Current AI agents are amazing, but they are mostly "amnesiac". Every new conversation starts fresh. They forget: previous decisions why something was chosen project history research important context relationships between information I want a system that acts like a long-term brain for AI agents. The ideal system would have: 🧠 Knowledge graph, not just notes Not a normal notes app or folder of markdown files. Knowledge should become connected: Projects Tools People Decisions Concepts Events Claims With relationships like: "Project A uses Tool B" "Decision X was made because of Reason Y" "This replaced the previous approach" "These two facts contradict each other" Basically a personal knowledge graph. 🤖 AI-powered ingestion It should be able to take: AI conversations documents code research notes and automatically extract: important information entities relationships decisions context 🔍 Better retrieval than normal RAG I don't want just: "Put everything into embeddings and search." Ideally it should combine: keyword search semantic/vector search graph relationships So the AI can answer questions like: "Why did we choose this technology?" "What projects depend on this?" "What changed compared to the old approach?" 📜 Memory history / provenance A very important part: The system should remember: where information came from when it was added what changed later why something is considered true I don't want the AI to silently overwrite memories. 😴 Self-maintaining memory Something like a "sleep cycle": remove duplicates find connections detect contradictions clean outdated information improve organization over time 🔌 AI agent integration Ideally: local/self-hosted free open-source runs on personal hardware API/MCP support would be amazing Things I am NOT looking for: ❌ A normal note-taking app ❌ A simple Obsidian alternative ❌ A chatbot with file uploads ❌ A basic vector database wrapper ❌ A paid SaaS memory service I'm looking for something closer to: "An operating system for AI knowledge and long-term memory." My questions: Does something like this already exist? What open-source projects should I look at? Is there any project that is worth extending instead of starting from zero? If you built something similar, what would you use? I’m completely fine with projects that are early, experimental, or not polished. I care more about the architecture and whether it can be extended. Thanks!212
OpenTool Request
Before I spend months building an AI memory/knowledge system, does a free open-source project like this already exist
Hey everyone, I’m about to start building something pretty big, but before I waste months reinventing the wheel, I wanted to ask this community: Does a free open-source project already exist that does something like this? If something close already exists, I would much rather use it, contribute to it, and improve it instead of building everything from scratch. What I’m looking for is basically an AI-native knowledge base / memory system for AI agents. The problem I’m trying to solve: Current AI agents are amazing, but they are mostly "amnesiac". Every new conversation starts fresh. They forget: previous decisions why something was chosen project history research important context relationships between information I want a system that acts like a long-term brain for AI agents. The ideal system would have: 🧠 Knowledge graph, not just notes Not a normal notes app or folder of markdown files. Knowledge should become connected: Projects Tools People Decisions Concepts Events Claims With relationships like: "Project A uses Tool B" "Decision X was made because of Reason Y" "This replaced the previous approach" "These two facts contradict each other" Basically a personal knowledge graph. 🤖 AI-powered ingestion It should be able to take: AI conversations documents code research notes and automatically extract: important information entities relationships decisions context 🔍 Better retrieval than normal RAG I don't want just: "Put everything into embeddings and search." Ideally it should combine: keyword search semantic/vector search graph relationships So the AI can answer questions like: "Why did we choose this technology?" "What projects depend on this?" "What changed compared to the old approach?" 📜 Memory history / provenance A very important part: The system should remember: where information came from when it was added what changed later why something is considered true I don't want the AI to silently overwrite memories. 😴 Self-maintaining memory Something like a "sleep cycle": remove duplicates find connections detect contradictions clean outdated information improve organization over time 🔌 AI agent integration Ideally: local/self-hosted free open-source runs on personal hardware API/MCP support would be amazing Things I am NOT looking for: ❌ A normal note-taking app ❌ A simple Obsidian alternative ❌ A chatbot with file uploads ❌ A basic vector database wrapper ❌ A paid SaaS memory service I'm looking for something closer to: "An operating system for AI knowledge and long-term memory." My questions: Does something like this already exist? What open-source projects should I look at? Is there any project that is worth extending instead of starting from zero? If you built something similar, what would you use? I’m completely fine with projects that are early, experimental, or not polished. I care more about the architecture and whether it can be extended. Thanks!015
OpenExploring Solutions
Still figuring out agent infrastructure- does the model eventually become the easy part?
I have been playing with the local-LLM side of things, once Qwen3.8-27B dropped yesterday... Ofc i spent the initial hours bawling over the benchmark numbers 😅 But ofc while all the running the model is good... For someone like me (grad student), what happens after you've got the model running becomes less obvious... By now the argument for "harness engineering" is really strong... Establishing that the durable engineering advantage may increasingly sit around the model, and that a better harness improves agent performance far more than simply swapping one good model for another. Qwen3. 8-27B feels like a good example of why that matters. Currently stacks like: Model - harness - tools - state/context - permission - eval - deploy I know I just wrote basic stuff😭 Me can figure out this stuff for 1 agent... But for people who actually get work done by locally running models or for companies that have 20,50,more agents: How do u manage and version them? How do u give each one scooped tool access? How evaluate, and actually observe their actual actions after deployment? How do u keep the whole thing manageable across machines/clouds? I'm curious as to what ppl here are actually using for this parts.... Is the ans basically DIY stack around llama.cop+ Langgraph/OpenCode + own tooling or are the different llatforms for agent-infrastructure and control plane doing good? I saw NVIDIA is going more runtime direction with NemoClaw and OpenHands has something on control plane, on ln I came across Lyzr and their no code control plane .... How much of those are branding and how much actual work? Would like to know ur views12
OpenComparison
Anyone using Cursor AI and barely writing any code? Anything better than Cursor AI ?
It feels like coding has shifted from writing syntax to just acting as an architect and approver. For smaller .NET/Blazor projects, letting Cursor handle the full context and just applying changes makes the process way faster. Copilot feels outdated after this, though I still use web-based ChatGPT to brainstorm initial concepts before bringing them into the editor. For those using AI-first editors daily, is anything currently outperforming Cursor on full-codebase context, or is it still in a tier of its own?76
OpenExploring Solutions
Am I going crazy or what?
A few lines about my pov: I have about 12 years of experience, mostly in game dev. I mostly use Unity as my goto environment just because I like it. But I dabble in many fields from all around the stack. Recently, all I have heard around everywhere is how good Ai is at coding, and it allready replaced coders competely. I use it on the daily too to make small functions and do some easy straightforward tasks for me. But anything with even medium complexity it fails at. Even the most expensive models fell apart as complexity rose. Yes, they might produce code that works "fine." But it's unreadable and waaay overcomplicated. Not to talk about that about 20% of the time, their response and code just don't make any sense. Are people who claim that AI is an expert level coder just never understood how to code properly, or did I miss something that unlocks this capability of the AI?40
OpenTroubleshooting
What should a coding agent remember: text, or the causal history of a successful change?
Most agent-memory systems store conversations, summaries, embeddings, or workflow checkpoints. Those are useful, but they usually lose the exact relationship between the goal, selected context, repository state, attempted actions, failures, validation, and accepted outcome. I am exploring a different memory object for software agents: an immutable, typed causal history joined to versioned artifacts. The rough model is: text goal - selected context - exact files and symbol versions observed - tools and delegations - attempted changes - failing validation - corrective action - accepted outcome - resulting artifact versions Small facts, identities, correlation, declared causation, and lifecycle transitions would live in an event ledger. Larger objects such as source files, diffs, context bundles, and model outputs would be content-addressed artifacts referenced by those events. Graph, vector, relational, search, and hot-cache databases would remain useful, but only as rebuildable projections. The ledger says what happened. Artifacts preserve what existed. Projections make that history useful for a particular query. The part I find most interesting is treating a successful historical subgraph as an exemplar. Instead of retrieving text that resembles the current task, the system could recover a previously successful transformation, compare its original dependency graph with the current repository, identify changed assumptions, and reuse only the structure that still passes current validation. This is a position paper and research direction, not a claim that the full architecture is implemented or that its individual ingredients are novel. Event sourcing, content-addressed storage, code graphs, vector retrieval, tracing, and workflow replay already exist. The question is whether joining them under one authority model creates a meaningfully better memory substrate. The smallest prototype I can see is one real coding-agent task: capture durable execution events and exact source state, join them into an execution and code-evolution graph, declare one successful slice as an exemplar, then attempt to adapt it after the repository changes. I would especially value pushback on three questions: 1. Is a causal subgraph actually a useful first-class memory object, or can existing episodic-memory representations provide the same value? 2. What is the minimum event set needed to reconstruct a software-agent transformation without creating an unusably expensive trace system? 3. Where should the boundary sit between immutable historical facts and mutable semantic interpretation?58
OpenExploring Solutions
Is anyone actually happy with their AI agent memory setup?
I've been building around AI memory for a while now, and one thing surprised me. Saving a memory is the easy part. Things get messy when the user changes their mind, two agents learn conflicting things, old information is no longer true, or you need to figure out why the system believes something in the first place. I originally thought a lot of this would just be embeddings + vector search + some metadata. It... did not stay that simple. I ended up spending way more time on conflicts, provenance, memory lifecycle and keeping things consistent across agents than I expected. I'm currently benchmarking what I've built before putting it in front of more users, but I'm curious how people here are solving this in real products. Are you using a vector DB and handling the rest yourself? Using one of the memory frameworks? Or just keeping memory pretty simple until you actually need more? Would genuinely like to hear what has (and hasn't) worked for people.30
OpenTool Request
Are there actually any usable tools for managing Shadow AI in companies?
I’ve been lurking for a while and wanted to ask something I haven’t been able to get a clear answer on. I’m trying to figure out if there are any real tools out there that can help with visibility around AI usage in a company. Mainly things like which AI tools people are actually using, who’s using them, and ideally what kind of data might be going into them. This feels a bit different from traditional shadow IT. With AI tools, I don’t necessarily want to block everything outright, since a lot of them are genuinely useful and people rely on them for productivity. But there are a couple of concerns that keep coming up for us, sensitive data potentially being pasted into external AI tools and people building quick scripts or “AI-assisted” internal tools that end up being fragile or insecure. the usual end-of-year rush where a lot of small automation tools get thrown together quickly and never really reviewed properly We’ve tried looking at existing security and monitoring solutions, but most of them don’t seem to give meaningful visibility into actual AI usage, especially when it’s happening inside approved tools or browsers. What I’m trying to understand is whether anything mature actually exists in this space yet, or if this is still something companies are mostly handling in an ad hoc way. Would appreciate hearing if anyone has actually solved this in practice023
OpenTool Request
Any local first setup similar to Lovable or Bolt for building AI apps?
I've been experimenting with tools like Lovable and Bolt for quickly putting together small apps and prototypes and the workflow is honestly pretty smooth. You describe what you want then get a working version then keep iterating through chat. but the longer I use them the more I run into limitations. the generated code gets messy over time and its harder to understand or maintain properly. it also feels pretty tied to the platform which makes it uncomfortable having everything live in a hosted environment from the beginning. What i'm trying to figure out is whether theres a similar approach thats more local first. something where the project ives locally instead of being locked in a cloud tool, I can use my own API keys or models, everything can still be opened and edited in a normal dev setup. I'm not dependent on a single platform to keep working on it. Has anyone here actually built projects like this? curious what people are using that gets close to this kind of workflow53
OpenComparison
Best AI coding tools for larger projects in 2026?
I know this kind of question comes up a lot, but AI tools change so fast that older threads don’t feel that relevant anymore, so I wanted to ask again based on my current setup. I’m working on a few hobby projects, some of which are fairly large C++ codebases in Visual Studio. Right now I’ve mainly been using ChatGPT (o1, not Pro). It works fine for smaller tasks, but once the project gets more complex or I need changes across multiple files, it starts to lose consistency or gives answers that don’t quite fit the full context. What I’m trying to figure out is what people are actually using for bigger projects where context really matters. Ideally looking for something that can: handle larger codebases without losing track of structure understand relationships across multiple files, not just single snippets help with debugging and refactoring in a more reliable way feel at least as capable as o1, if not better Budget isn’t a huge issue, but I’d prefer something around the $20/month range if possible since I had mixed results with higher tiers before committing. What’s actually working well for this kind of workflow right now?914
OpenExploring Solutions
how hard are people actually maxing the flat-rate usage on their AI plans?
One of my projects came out to $1,492.42 API-equivalent at rate-card pricing (this is in past 2 weeks), props to Codex resets!! this was on Claude Pro and Codex plus plans too, so basically ($40 plans in total) genuinely curious what people on the higher tiers MAX plans are managing to squeeze out of them!!10
OpenExploring Solutions
Claude Code in WSL safe?
Basically the title. We are looking into different options to make work with Claude Code safe within our company. We considered WSL for local development, or server hosted docker containers with remote dev access. What did others think about and test? What is a good/useful but safe place to work?017
OpenComparison
Any under rated coding agents
Besides the obvious like Codex and Claude Code, Are there any coding agents that you guys actually use on a day-to-day basis that you find is actually more worth than these two？ I hear things about OpenClaw, Hermes, OpenCode all the time, Can anyone here speak to the the quality of using these agents, as opposed to Codex and Claude Code.And considering you canplug in many open source LLMs into Codex. Is it even worth it using these other harnesses?526
OpenTool Request
Building a local-first opensource pentesting harness — looking for architecture advice I’m a cybersecurity/AI student and I want to build a local-first agentic harness specifically for bug-bounty.
The rough idea is: User prompt → AI agents → planning/orchestration → MCP/tool layer → isolated pentesting environment → results → analysis/report Hacking via prompt 🔥 Ik there are many existing platforms but I wanna build on my own The system would then decide which tools to use, execute them through MCP, analyze the results, adapt the next steps, maintain context/findings, and eventually produce a structured report. I’m currently thinking about supporting Kali/Exegol-style environments and tools such as Nmap, Nuclei, ffuf, Burp, SQLMap, Metasploit, etc. I’d like the model layer to be free/local-first, probably using Ollama The problem is that I’m getting overwhelmed by the architecture. 😅 I’m unsure about: \- Which orchestration pattern makes the most sense for pentesting \- Whether I should use LangGraph/CrewAI/etc. or build the orchestration layer myself \- How to structure specialized agents (recon, web, exploitation, analysis, reporting, etc.) \- MCP architecture and how granular the tools should be \- Safe execution/sandboxing for shell commands and security tools \- Scope enforcement so agents cannot accidentally touch unauthorized targets \- Guardrails and approval gates for dangerous actions \- Memory, state management, findings/context persistence \- Browser + internet research capabilities \- Observability, logging and replaying agent runs \- How to evaluate whether the agent is actually making good security decisions \- What a sensible MVP should look like instead of trying to build everything at once For people who have built AI agents for security/pentesting: What architecture/pattern would you recommend for this? What would you absolutely include in the MVP, and what would you leave out initially? Also, if there are existing open-source projects, frameworks, MCP servers, sandboxes, or agentic patterns I should study before writing my own, I’d really appreciate pointers. Any good tips on coding this harness that most developers tend to overlook?45
OpenComparison
best helicone alternatives for enterprise teams: arize, orqai, langfuse, humanloop ,fiddler, honest breakdown
been using helicone for a while now and tbh, it works fine for individual devs and small setups but will it be that efficient when team goes bigger.. like sso,rbac, audit logs, data residency, access control. enterprise teams often initially ask for all of this and i genuinely dont know if helicone goes there or if people just move on at that point… so am trying to know what people are actually using at the enterprise levevl spent around a whole day in it and i found a few names arize: strong on monitoring model and evals. its enterprise covers compliancee requirements .open source version has no gdpr or soc2 so not sure on the right tier before assuming anything orqai: gdpr and soc2 coverred, has rbac and eu data residency options. newer than the other so ecosystem and third party integrations are still catching up langfuse: an opensource with solid tracing and an active community. soc2 and gdpr covered with a dpa available. self hosting is also possible which often enterprise teams love but setting it up and maintaining isnt going to be easy fiddler: built specifically for governance and compliance according to industry standards, audit evidence and risk control are native. feels heavy and expensive if your team doesnt actually need that kinda depth humanloop: soc2 gdpr and hippa covered, has rbac and vpc deployment options. more focused on prompt management and evals than observability so depends what your team actually neeeds so asking if you moved away from helicone specifically because of enterprise requirements what did you land on and did it actually cover your requirements22
OpenTool Request
An MCP Server / agent that understands your codebase
We use a local mostly C and typescript coded framework. We do not plan to host it on GHE or a cloud solution. Is there a tool or skill using MCP / sub-agent (A2A) our coding agents can use to get information about this codebase? Effective but costly is tp provide the source code and documentation of that library. But with agents in parallel, sandboxing, worktrees this becomes costly in respect to storage and tokens. Is there an open solution which I can feed a git repo and documentation and serves as an "oracle" for a coding agent? I am aware of Do you have experience with any of these tools or can recommend another fitting tool?01
OpenTool Request
Build a tool with claude code for users working with claude code; feedback appreciated
It's evidence layer for people building with 10 parallel agents. It takes you out of the loop for manually checking everything your agents build; and verifying if it works or not. It runs on your app / PR; identifies the intent automatically, runs your app on a real browser and tries to imitate the flow, and reports back what worked and did not with visual evidence. Here's the GH repo - What do you use for checking if agent created a working solution ?01
OpenTool Request
The build step is easy now, the deploy step is where every small tool dies
I've been watching a pattern with everyone building small tools with AI (lovable, bolt, cursor, claude code, whatever) and it's always the same failure point. The thing gets built fast. you're proud of it. then you go to actually show it to someone and all you have is localhost. So you consider deploying to some cloud provider, probably Vercel, but that not only feels like an overkill, it means opening it up to the public unless you build auth. Two things i've come to believe from this: 1. the deploy/hosting step is the actual bottleneck for small software now, not the building. Building got cheap. the last mile didn't. 2. Personal hosting feels like an unsolved problem. I imagine a world in which ordinary people build, deploy, and host personal tools the way they make a spreadsheet today: for themselves, in an afternoon, without asking a developer and without ever seeing a server. Curious how others here are handling this. when you build a small internal tool or side project, what's your actual path from "it works locally" to "a real person can open a link and use it"? and how do you deal with the free tiers evaporating (railway, fly, netlify all gutted theirs)?07
OpenExploring Solutions
agent caught its own broken fix before it merged
agent caught its own broken fix before it merged, a gate that can actually say no gave the agent one vague prompt: "users noticing a billing issue on prod, find fix and prove." it audited the service, found 22 bugs ranked by blast radius, wrote a fix, then ran it through the sandbox. its own SQL-injection fix failed the proof. so it diagnosed it, stripped the over-engineering, and re-proved green. no human in the loop, no prod creds, no "trust me it compiles." that last part is what fetchsandbox is actually for. your agent writes the stripe/webhook/auth integration, it looks fine, returns 200, passes review, then breaks on duplicate webhooks or out-of-order events in prod. the sandbox reproduces those scenarios against your actual code before anything merges. bug reproduced, fix verified, receipt url, not a vibe. full 4-min demo in comments. wondering if anyone else has a setup where the agent can actually fail its own fix.14
OpenExploring Solutions
An interesting development approach(compatible with Pi)
A spec-gated build loop for AI coding agents — and why porting it to Pi/oh-my-pi wouldn't be hard Found ai-blueprint this week and it's worth a look if you've been hand-rolling your own guardrails around Claude Code, Codex, or Pi. Brad Traversy built it as a workflow overlay, not a framework: you scaffold your app normally, then drop this on top. The mechanics are simple. You write two short files — what you're building and why, then a rough ordered feature list. A /overview command turns those into the single context file the agent reads every session. From there it's a loop: /feature writes a small buildable spec and stops. You review it. /implement builds one step at a time, shows you the diff, waits for approval before touching the next one. /audit reviews the code (not the behavior — that's /check's job) and writes findings to a ledger with durable IDs like F-03. /complete archives everything and merges, but only after your go-ahead. The part that actually made me sit up: that findings ledger isn't decorative. A P0 or P1 finding sitting open or fixed blocks /complete outright. Not "the agent decided it was fine" — a status field the merge step is written to check. And they didn't just claim it works. There's a live-agent end-to-end harness in the repo that spins up a real agent against a fixture project and asserts the ledger gate actually holds. I don't see that level of rigor in most prompt-engineering repos. Most people ship a pile of markdown and call it a system. Now the gaps, because none of this is free. The gate is enforced by the same model reading its own instructions in the same context that just did the implementation work. There's no independent process outside the agent's own compliance that blocks a bad merge — it's a very well-written contract, but it's still a contract the agent has to choose to honor. A confused or context-poisoned run can walk right past it, and nothing server-side stops that. current-feature.md is also a singleton. One feature in flight at a time. That's a fine constraint solo, but it doesn't obviously extend to two people, or two agents, touching the same repo concurrently — the state model just isn't built for that yet. And the defaults lean hard into one stack. coding-standards.md ships assuming Next.js, Prisma, Tailwind, Zod. /onboard is supposed to retune it, but that's one more step that can quietly go stale on a project the agent didn't actually read closely. It's also young. First release was early July, one maintainer, 239 stars — which honestly reads more like Traversy's existing YouTube audience showing up than a track record on a gnarly, multi-year codebase. Worth watching, not yet worth betting a real team's workflow on. Here's the part I think matters for this sub specifically: none of this is Claude-Code-specific. It's markdown files, plus two thin adapters (.agents/skills for Codex, .claude/skills for Claude Code) that both read one shared AGENTS.md. Pi and oh-my-pi already speak most of that language — AGENTS.md context files are supported, skills show up as /skill:name, and the extension API can register custom tools and commands. Porting this loop into an oh-my-pi extension pack looks like a weekend project, not a fork. So — has anyone here already been doing something like this by hand in Pi? And does Pi's whole "minimal, adapt to your workflow" philosophy actively resist something this opinionated, or is that exactly the gap an extension should fill? \PS: Used AI to draft out the above based on my takeaways and thoughts from initial read of this repo.04
OpenExploring Solutions
Which security gates enabled for AI Agents in CI/CD?
We've become pretty comfortable putting conventional applications through CI: dependency scanning SAST CodeQL secret scanning container scanning IaC checks security policies ... But what happens when the application being deployed is an AI agent? That may not look particularly interesting in a conventional code diff. But from a security perspective, it could be a significant change. I'm experimenting with a different CI question: “What capabilities changed in this PR?” \-- We've implemented an early version of this approach in an open-source static analyzer and connected it to GitHub Actions. (ikaruscareer/SafeAI at GitHub) The scanner runs locally against the repository and doesn't execute the agent or send the source to a remote service. I'm curious how other teams approach this.20
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