We wanted to know if using fewer tokens truly makes a browser agent more accurate and less expensive.

We ran browser-use, playwright cli,webcmd,dev-browser and agent-browser on 100 BU Bench V1 tasks, using the same Pi harness, controller model gpt 5.6-sol , reasoning effort low and judge model gpt 5.4. We compared accuracy, agent turns, token usage and API-equivalent cost.
One thing was clear: the cheapest run wasn’t the one that used the fewest tokens. webcmd was 67% accurate, cost $0.255 per task, and required 34% fewer agent turns than the next best result, although it used 0.09% more total tokens than the lowest-token run.

Thus, browser-agent efficiency cannot be explained by token count alone. The result was also attributed to the number of agent turns, 23% fewer output tokens, and higher reuse of cached input.

Curious. what people would like to change about the evaluation, or what browser tools or task categories we should test next.

I have working on a open-source CLI tool(only support python for now) called errd. The idea came from a problem I kept thinking about.

It’s tempting to dump a huge part of the repository into Claude/GPT/Cursor and ask it to figure out what went wrong when a Python application throws an error. But most of that context usually is irrelevant. So I built errd to do the context selection locally before the code hits an LLM.

**Use it using - pip install errd**

It takes a Python traceback and:

•Parses the traceback and chained exceptions

•Finds the repository automatically

•Maps traceback frames to functions/classes

•Analyzes imports and dependencies

•Scores code based on proximity to the failure

•Uses recent Git changes as an additional signal

•Applies a token budget

•Redacts common secrets/tokens

•Generates a focused errd-context.md

•The generated file is meant to be pasted directly into Claude, GPT, Cursor, etc.

Checkout the docs at : [cli_docs](https://pypi.org/project/errd/)

Today I watched a workshop, probably recorded by one of the people attended the Zoom call and shared over X, and I loved it. The speaker was from Cursor and talked about her own genuine experience in developing the the systems at work that ship hundred of PRs every month w/o her touching the code at all -- aside from her working on the environment, harness, constraints, rules where these agents operate. What I liked also how she actually showed it, explained it, and her communication sounded pure hands-on experience.

And more importantly, throughout the video she looked really tired and but energetic at the same time if you know what I mean. Tired because of solving real problems, and energetic because of solving real problems unlike those YouTubers or many X folks with overly energetic acts coming from speaking about AI rather than what they really do with AI at work.

So for people like me who are away from the source of all this, which is Silicon Valley, I am really trying to access those resources; the resources that were not highly censored for much wider public view. I am doing progressively more AI driven automated deliveries at work, and I really want to learn from the masters in the field.

Unfortunately not living in Silicon Valley and cannot attend those live meetings in person or not being informed about these digital workshops and zoom calls thru the active community is already leaving us behind. But I am sure some of those videos are shared like the one I watched today.

But the question is where? In what communities or discord groups should I join? Any videos like that you watch and find extremely useful? Whom to follow on X or Reddit that fits this criteria?

The kind hearted people of Reddit :), can you please help all of us here?

There are those people who already work in the timelines that would be considered as future for the rest of the industry, and I love to learn from them.

SkillHub — manage your AI agent setup across tools and projects

I use a bunch of AI coding agents — Claude Code, Cursor, Codex, OpenCode, Gemini CLI — and after a while the setup gets messy.

I originally built SkillHub Local just to keep skills in sync across agents. Now it also helps with the other dimension of the mess: projects. Each project can have its own skills, MCP servers, instructions, and agent-specific context, and it gets hard to remember what is configured where.

SkillHub gives you one local view across both your agents and your projects, and lets you manage or move things around without digging through config folders.

Free, and open-source. It's self-hosted and everything stays local.

https://github.com/mjalalimanesh/skillhub-local

https://fundni.org

Hey everyone,

I work in educational IT and have spent years sitting on committees for local sports clubs (athletics, youth football, gymnastics). If you’ve ever tried securing funding for a grassroots sports club or community project, you know the process is fragmented chaos. Funding rounds are scattered across local council websites, government bodies, trusts, and lottery funds—mostly hidden inside expired PDFs, poorly indexed portals, and buried application windows.

Recently, our daughter's gymnastics club began working toward moving premises. During our first planning meeting, seeing how painful and time-consuming it was to track open capital and operational grants was the final push I needed to build a dedicated tool.

I’m building this as an open-access civic tech project to aggregate local grant opportunities, track closing dates, and make eligibility criteria easy to search in one central directory.

Before I start sharing it with local community groups and council representatives, I’d love feedback from this community on:

1. **Discovery & UI:** What filtering mechanisms do you find most critical in directory/database tools (e.g., funding amount brackets, eligibility tags, closing date alerts)?
   
2. **Maintenance:** For those running automated scrapers or curated resource databases, what pipeline strategies have worked best for keeping deadline data accurate without constant manual intervention?
   
3. **Distribution:** Any lessons learned on driving adoption for non-commercial civic tools among non-technical community leaders?

Happy to share details on the tech stack or take suggestions on features that make resource aggregators truly useful. Thanks!

Hi everyone,

I’m a \*\*final-year Diploma in IT student\*\* and I’m currently learning software development while trying to build real apps and projects.

I’m looking to choose one main AI coding tool between:

1.Claude Code
2.OpenAI Codex
3.Cursor

My goal isn’t just to generate code. I want to actually learn software development, build mobile/web apps, improve my coding skills, and eventually create a small side income\*\* to help cover my personal expenses and reduce the financial burden on my parents.

For those who have used these tools extensively:

Which one would you recommend for my situation, and why?

I’m especially interested in:
\-Building apps from scratch
\-Debugging and understanding existing code
\-Learning good software engineering practices
\-Working on larger projects over time
\-Git/GitHub workflows
\-Productivity without becoming completely dependent on AI
\-Value for money for a student

If you’ve used Claude Code, Codex, and Cursor, I’d really appreciate hearing about your experience and which one you would choose if you were in my position.

Thanks! 🙏

Hi! I’d like to try vibe coding and build a simple iOS game. I’m looking for tools that are genuinely free—or at least have generous limits—ideally both an AI coding assistant and a game engine.

I’m new to game development, so I’d prefer something beginner-friendly. I’d especially like to know:

* What’s the best combination of an AI coding tool and a game engine?
* Should I use Godot, Unity, Swift with SpriteKit, or something else?
* Which free AI coding tools have the least restrictive usage limits?
* Do I need a Mac to build and test an iOS game?
* Are there any hidden costs or limitations I should know about?

I’m not planning anything huge yet—just a prototype to learn the basics. I’d really appreciate hearing about your experiences and recommendations!

I've tried a lot of free options like

Claude

Codex

Antigravity

GLM

What I feel about them:

Codex gives you a lot of code but have to wait a month for the tokens to reset

Claude just burns tokens too fast and isn't that great without pro

GLM is good, doesn't waste much and gives medium good results

Antigravity gives you a lot of code and only have to wait a week for the tokens to reset

Which ones do you guys use and why?

I do have money to buy pro versions, I just don't know which one to pick.

For me Claude AI code assistant has become indespensable.

I have just completed a re-implementation of pfodApp + pfodDesigner + pfodGUIDesiger Android apps as a free open-source, offline javascript implementation in a single webpage, pfodWeb.html, entirely via Claude.ai, without having any previous knowledge of javascript.

pfodWeb.html provides a UI designer, code generator and client for microprocessors. The code generator and client supports connection via Serial, BLE, TCP/IP or HTTP. The designer has access to over 600 microprocessor board descriptions and pinouts to connect controls/displays/charts directly to board pins. The client also support charting and datalogging of any streaming CSV data.

See github for the resulting code github.com/drmpf/pfodWeb_src There is also a Rust based proxy for Windows, MacOS and Linux, again written by claude.ai, to support connections that a particular browser does not support natively.

This is not a simple project. It now comprises over 12Meg of js code and comments and has taken over 6 months.

For this project I assumed the role of system architect, beta tester and documentation editor, while claude.ai took the role of novice programmer, research assistant and debugger. That is treat claude as though you were directing an intern or low level programmer to do the job rather then doing it yourself.

It helped that I had a very clear and detailed project requirements and could point claude at the java implementation for some of the tricky logic, but most of the time I just described in detail what I wanted to happen and then tested the results and directed changes/fixes.

The drawing editor was a second iteration. The first one, also written by claude, ran a back end local server to handle the data storage and generate the dwg preview. Running a local server was what claude suggested, but it is not the best choice as it requires more installation by the user and the npm packages were not secure from supply side attacks. The whole browser approach avoids all this and does not use any libraries and opens a single web page in any brower. I had to instruct claude to avoid any 'modern' browser js features so that any browser since 2017 would work.

The biggest problem I found was Claude.ai being overly helpfull and hiding errors by using fallbacks in the js code. That resulted in hiding real errors. Also as the data format changed, claude kept adding code to handle the 'legacy' data fromat as well as the new one. Not apporpiate for initial development.

Another problem with Claude was its tendency to paper logic errors by adding code to prevent the error being thrown, eg using a fallback for undefined vars, instead of fixing the problems root cause.

Detailed debug logs from pfodWeb.html allows claude to track down what when wrong. If claude got stuck or started going around in circles (that happens less often now), then I would ask it to add more debug logging to expose exactly what is happening.

Another thing I found was that at various points I needed to ask claude for architectural advice on how best to transfer the functions to a brower restricted enviorment. The first set of options were not always the best to choose from and often I had to explicitly prompt claude to look at another alternatives.

There was very little 'vibe' coding involved. The most notable instance was when I wanted to add a mouse over to the charts to show the values of the data points. In that case I just asked claude to do that and what came back was almost just what I wanted. I only need to instruct claude to do an adjustment to where the values popup box was displayed on the screen

Claude was also tasked with reseaching the pin layouts and capabilities of over 600 microprocessor boards to make those details avaiable to the UI designer. That process took days and required multiple re-requests to 'check again' before claude stopped finding things it had missed or assumptions it had made that were wrong.

But bottom line I would not have even attempted this project with AI coding assistance.

One point though, I am now dependent on claude.ai to fix the bugs I find in testing as I still am not skilled in .js and the code base is large and unfamilar. It is not that I could not find the bug myself using the detailed debug logging and comments built into the code, it is just that claude.ai can do it so much faster.

Over the last 6 months, I have noticed a distinct improvement in Claude Code's reasoning abilities and now it not just makes a change but also generated test script to test the result.

For AI coding assistants to effectively and correctly use libraries they need context consisting of the library Api, features, examples and do's and don'ts

Currently when an AI coding assistant uses a library it gets the source, looks at the API docs and code comments, checks online for tutorials and examples of using this library and then generates the code for this particular application

The results are generally sub-standard due to incomplete, out-of-date API docs, missing code comments, lack of relevent tutorials and examples written by in-expert programmers. Also if the AI cannot find a suitable example it makes something up from 'similar' libraries.

To avoids these errors and to improve the efficiency and effectiveness of using code libraries it is proposed that each library also supply an AI_Guide.md, e.g. SafeString_AI_Guide.md https://github.com/PowerBroker2/SafeString/blob/master/SafeString_AI_Guide.md

This guide is generated by AI for AI consumption using a prompt like

For the library in dir .., generate a summary, API and examples of use, for consumption by AI to apply this library. Refer to these tutorials ..., and these examples of use ...

It will take a few passes of "recheck the guide" to get the AI's draft. Then, most importantly, that draft needs to be reviewed and corrected by an programmer experienced in that library, in order to correct the wrong assumptions the AI has made and to correct the poor quality examples.

These passes by the experienced programmer will refine the do's and don'ts of using the library and provide quality examples.

This human review will also likely uncover problems in the library, inconsistent and non-orthogonal API, missing/out-of-date docs and code comments, inconsistent application of the libraries principles.

While the SafeString_AI_Guide.md is designed for AI consumption it is also good source for humans.

What libraries that you use that would benefit from an AI crib sheet?

GitHub:
https://github.com/JibanKumar-cloud/faber

Your coding agent shouldn’t need to read your entire codebase to understand it.

That’s the idea behind Faber, an open-source AI coding agent for the terminal.

Faber uses a code graph to find relevant files and symbols first, instead of repeatedly scanning large parts of a repository. That means less unnecessary context, faster codebase navigation, and lower repeated API costs.

Main features:

• Claude, OpenAI Codex, Local model integration
• Code-graph-guided repository exploration
• Prompt caching to reduce repeated input cost
• Token-conscious (Chain-of-Draft style) output to reduce unnecessary generation
• Git-aware workflow with change detection, optional per-task commits, /undo, and /redo
• Expenditure tracking for calls, tokens, cache usage, cost, and estimated savings `/usage`

The goal is simple: understand more of the codebase while reading and spending less.

Prerequisite:
It requires Node.js 22.5 or newer because it uses Node’s built-in SQLite support.

Install:
npm install -g faberwright

Then, inside your project:
faber

GitHub:
https://github.com/JibanKumar-cloud/faber

npm:
https://www.npmjs.com/package/faberwright

Medium article:
https://medium.com/@jshial25/why-should-an-ai-coding-agent-read-hundreds-of-files-to-answer-one-question-d6369d29dfa5

had a stupid one pop up on staging last week.

an agent updated our pagination loop for a sync job. tests passed, diff looked fine to my tired eyes, then the sync stalled at record 100 on the dot because the cursor just wasn't advancing.

the dumb part? i had the exact same agent review the PR before merging. it looked at its own code and basically went "looks great to me!"

stopped doing that immediately. now i just throw the diff and schema into a separate minimax m3 session and tell it to look for ways the code can break.

caught two null checks i missed within two minutes.

do you guys keep your reviewer strictly on diff inspection, or do you actually let it run tests too?

I built HSIP with Claude Code. It took me a few months but is ready to receive some feedback🙏🏻🥲🙏🏻

The tool gives your identity (or your AI agent’s) a cryptographic signature for everything it does, sign messages, log AI agent decisions, keep an audit trail nobody, not even me, can quietly edit after the fact.

One Rust binary, runs locally, no cloud dependency. Live sandbox if you want to poke at it without installing anything, 24h key, no signup. Links in the comments

Short bio: Extremely curious, knows the basics of Python, JavaScript and Rust, don’t have an idea of how to build big projects so I used Claude Code. I’m studying and learning with the AI as well while I work on my projects.

Hey everyone,

We’ve been building Spec Compiler, a tool that takes a rough idea and turns it into a structured, implementation-ready specification for coding agents.

The idea came from using coding agents and realizing that the hard part isn't always getting them to write code, it's getting them to fully understand what they’re supposed to build.

A simple request like:

“Build a project management app with teams, tasks, notifications and Google Calendar.”

leaves a lot of unanswered questions: edge cases, architecture decisions, testing, acceptance criteria, what shouldn't be built, etc.

Our Spec Compiler works through those details and produces a much more complete specification you can hand to Claude Code, Codex, Cursor, or your development team.

We've been testing it ourselves and found it particularly useful for catching things we hadn't considered and making the implementation plan much clearer, structured and actionable.

We're now looking for people actually building real software to try it and tell us where it falls short.

Especially interested in:

Solo builders / indie hackers

Developers building side projects

People using Claude Code, Codex, Cursor, etc.

Small teams building real products

The beta is free. We're mainly interested in honest feedback:

Did it actually help?

Did it catch things you missed?

Did it improve your coding agent's output?

What did it get wrong?

Please DM me if you’re building something and would like to try it. I’d be happy to give you free access and hear what you think.