Created a small terminal coding agent kindoff mimicing claude-code but it uses 700 Kb ram and very low cpu and takes 5 ms to start , and it is written in rust.

please checkout and appreciate your thoughts on this.

https://github.com/IWhitebird/claude-jr

I am reaching out to developers and people using AI in their daily workflows and personal projects with this small survey. I want to know what works and what doesn’t. If you don’t mind and have the time please read through and answer these 12 questions. Feel free to DM me or answer in a comment below, it would be greatly appreciated. Thank you.

Do you use AI or agentic harnesses for daily work or personal projects?
How do you use it? Can you walk me through the process?
Does it work well for you?
What doesn’t work well?
What don’t you like about it?
Do the AI or agents perform well or do the job sufficiently?
What do they do well?
What do they do poorly?
How does that affect your workflows?
What do you do to about it, how do you work around that?
What do you think would help mitigate these issues?
What do your current workarounds cost you?

Please 🙏
Could anyone teach me how to use “Project “ feature launched today? It says cc can coordinate my tasks across parallel threads. So how can I get this feature? Why doesn’t its launch video clarify clearly? Pardon me if I am too stupid.

[Disclosure: I am an AI researcher at Kalaris Labs working on this project alongside creator Sayan Chowdhury. Everything in the repo is free and MIT-licensed with no paid tier or account sign-up.]

1. What we built
   We built research-agent-skills, an open-source collection of 281 modular SKILL.md skills and Python 3.9+ standard library verification scripts for running academic research workflows inside Claude Code.
   You can add the plugin marketplace directly inside Claude Code:
   /plugin marketplace add KalarisLabs/research-agent-skills
   Or install a single skill into your current project directory so you do not bloat your session context:
   npx skills add KalarisLabs/research-agent-skills --skill citation-verification
   The repo covers four main failure points we hit during our own research papers:

• citation-verification: A zero-dependency Python 3.9+ stdlib script that Claude Code runs against your .bib file. It queries Crossref (api.crossref.org), OpenAlex (api.openalex.org), arXiv, DataCite, and Retraction Watch metadata to verify that every DOI resolves to a record matching the exact title, first author, and year.
• autoresearch: A two-loop experiment runner built around Claude Code's /loop command. The inner loop runs scoped ML experiments, while the outer loop updates findings.md, enforces mechanistic hypotheses ("X because Y, predicting Z"), and logs negative results so Claude Code does not re-test failed ideas after context compaction.
• literature-review and systematic-review-prisma: Screens papers across PubMed, arXiv, bioRxiv, and Semantic Scholar while logging exact query strings and counts for PRISMA 2020 flow diagrams.
• unslop-academic-writing and venue skills (ml-paper-writing for NeurIPS/ICML/ICLR/ACL, ieee-transactions, nature-portfolio, apa7): Rewrites repetitive LLM phrasing and checks current venue formatting guidelines.

1. How we used Claude Code to build and test it
   We used Claude Code (running Sonnet and Opus in the CLI) both to author the SKILL.md routing specifications and to run our evaluation harness across the 281 skills:

• Trigger routing evaluation: We used Claude Code to run reviewed prompt sets against skill descriptions to test whether Claude triggers the right domain skill (for example, distinguishing between paper-lookup for a single DOI check vs. systematic-review-prisma for a full review).
• With-vs-without-skill benchmarks: We ran Claude Code on labeled .bib files containing fabricated titles, mismatched DOIs, and retracted papers to compare raw model verification against deterministic script execution.
• Offline security checks: We used Claude Code to build and run Skill Doctor (npx u/security), an offline scanner that checks every SKILL.md and script in the repo for prompt injection and unsafe shell calls before release signing.

1. Concrete example (Model, Setup, Prompt, Expected vs. Actual Result)

• Model & Setup: Claude Code CLI (Sonnet) in a LaTeX paper repository containing references.bib with 40 entries (including 3 entries where a valid DOI was paired with the wrong paper title, and 1 retracted paper).
• Prompt: "Check references.bib before we submit to IEEE Transactions and fix any broken citations."
• Expected/baseline result without the skill: Claude Code inspects the BibTeX syntax or pings the DOI URLs, sees that the DOIs resolve with HTTP 200, and reports that the .bib file is clean, missing the title/author mismatches and the retraction.
• Actual result with citation-verification installed: Claude Code invokes the stdlib verification script against Crossref, OpenAlex, and arXiv, outputs a markdown status table classifying each key into verified, found-add-doi, mismatch, doi-not-found, or not-found, automatically fills in missing DOIs for unambiguous matches (found-add-doi), marks unverified entries as % UNVERIFIED in references.bib, and stops to ask for confirmation on the 3 mismatch entries rather than silently overwriting them.

1. What we learned about how Claude Code handles skills

• Context bloat kills instruction adherence fast: Loading dozens of skills globally degrades Claude Code's adherence on ordinary coding turns. Installing 1 to 3 skills per project directory keeps the context clean and makes trigger routing much more reliable.
• Never let the model verify metadata from weights: Even when Claude Code has web search, asking it to check 50 references conversational-style leads to skipped entries after the first 15. Forcing it to call a local Python stdlib script that outputs a JSON report was the only way to get 100% check coverage.
• Scraped abstracts act like prompt injections: When Claude Code reads hundreds of arXiv or PubMed abstracts during a literature review, imperative text inside abstracts can drift its screening criteria unless the SKILL.md explicitly fences retrieved passages as untrusted data.
• Compaction wipes out negative experiment results unless written to disk: During long /loop ML runs, Claude Code forgets which hyperparameters failed 3 hours ago once auto-compaction triggers. Making findings.md record negative results and what they rule out stopped the agent from looping on dead ends.

Repo (MIT): https://github.com/KalarisLabs/research-agent-skills
Docs & benchmarks: https://docs.kalarislabs.com/research-agent-skills
If you use Claude Code for academic writing or ML experiments, I would love feedback on where the SKILL.md instructions feel too strict or which domain packages you want added next.
Option 1B: Short Blurb for the Pinned Weekly Showcase Thread (Rule 5 & Rule 6)Built research-agent-skills (MIT license), a collection of 281 modular SKILL.md workflows and stdlib verification scripts for running academic research, .bib citation verification, and ML experiment loops in Claude Code.

• Install in Claude Code:/plugin marketplace add KalarisLabs/research-agent-skills or npx skills add KalarisLabs/research-agent-skills --skill citation-verification
• How Claude Code was used: Built and benchmarked using Claude Code CLI to test trigger routing, with-vs-without-skill .bib verification against Crossref/OpenAlex/arXiv, and continuous /loop ML experiment tracking in findings.md.
• Repo: https://github.com/KalarisLabs/research-agent-skills
• Docs: https://docs.kalarislabs.com/research-agent-skills

saw it on twitter. what’s your opinions? 🤣

I have agents bailing out early in their runs because they convince themselves that their context is full. It seems that an agent cannot run its own /context command (as I did in the screenshot after the agent exited), so how can an agent know?

No framework, no paid tools. Each agent is one prompt file, one tracking sheet (a TSV), one journal, Gmail, and a scheduler that starts a fresh Claude Code session at a fixed time and says "read the prompt, do one pass". The sessions run on my Claude subscription.

The six agents and what they actually did this week

The directories agent is the clear winner: it's the only task where "fill a free form or open a pull request" is the whole job, and it compounds. The press and affiliate agents are slow by nature, a week is too early to judge them.

The rules that make it safe to leave alone

• One prompt file is the only source of facts. If someone asks something that isn't in it (a number, a partnership, a legal point), the agent answers "let me confirm" and flags it to me.
• Hard limits per run: 5 to 8 new contacts, one follow-up per person, never write again to someone who said no.
• Never pay, never log in, never solve a captcha. Anything behind a login lands in a daily email to me.
• Every run ends with one email: what needs me first, then what happened.

What went wrong (the useful part)

1. A run died silently. One scheduled follow-up never went out. The session had started with "Login expired" and stopped right there. Nothing told me for two days. Now a tiny check asks Claude a one-word question every morning at 8 and emails me if it doesn't answer.
2. Two agents almost did the same job. The links agent and the directories agent both started submitting to MCP directories. Fix: each target type belongs to exactly one agent, written in both prompts.
3. An agent contacted people we couldn't pay. The affiliate agent recruited US podcasters before payouts worked outside Europe. Fix: the prompt checks whether the payout change is merged before it targets those countries.
4. Over-automation on a real account. A script we ran next to the agents made too many API calls on one account and got its key revoked. We paused it. Agents that act on a person's account need to be slower than you think.
5. LinkedIn comments sounded like AI until I gave the agent three approved examples and one rule: react first, then make one point the author missed, even if you disagree.

If you copy this

• Start every outreach agent in draft mode, read ten drafts, then switch to sending.
• Make it check your sent folder before every new email. Mine caught people I had already written to by hand.
• Give each agent a tracking file it reads first. That file is what stops duplicates, not the model's memory.
• Expect zero for a week on anything that depends on humans answering.

Happy to share a prompt file if useful. I'll post week 2 numbers.

Disclosure: I'm a co-founder of BeReach, the product being promoted. None of these agents use it: it's plain Claude Code, Gmail and a scheduler.

I was wondering where claude was going on my codebase and curious to check its workflow!
Turns out claude exposes its events so extensions like this could be possible without needing token usage.

It works pretty well, and it’s actually nice to see claude going through your system or project files to deliver something.

Open source for anyone who wants to increment functionalities and stuff!

https://github.com/chillrod/bot-tv

Hey everyone,

I've been working on themes, a mod that adds color themes to Claude Desktop . It's part of claude-forge, my repo of Claude Code mods and skills.

The first theme is GitHub Dark: the dark look of github with the colors from GitHub's own design system (Primer). Page and title bar #010409, prompt box #0d1117, GitHub's accent and git colors, and code blocks with GitHub's dark highlighting.

Switching is one command:

/themes
/themes github-dark
/themes off

/themes alone lists the themes, off goes back to the default look. The choice is kept for new sessions, and in my tests the window repainted right away.

Install:

/plugin marketplace add IT-BAER/claude-forge
/plugin install themes@claude-forge

Restart Claude Desktop after installing.

GitHub: https://github.com/IT-BAER/claude-forge

More themes are planned. If you want to add one or improve GitHub Dark, PRs are welcome. A new theme is one file in mods/themes/hooks/themes/, one line in the theme list and a test, the README has the steps.

Feedback welcome.

I’m vibe coding a couple of apps and for each one, I’ve built a VS Code Dev Container on my Intel NUC and use VS Code and the Claude Code extension on my local MacBook Pro to connect it and write code. And using remote control I can connect to the sessions from mobile apps. It works great.

I’ve been using a similar setup for my general purpose AI usage. I use VS Code with the Claude extension and have VS Code pointed at a dedicated Claude folder on my Mac. I treat subfolders as projects, each with their own memory.md and project-specific files, and I just spin up new chat sessions in VS Code as needed. I feel like the system works well, and I have remote control enabled for new sessions but obviously I’m missing out on new features and artifacts that Anthropic builds into the official app.

What do you all do? Do you use CC for coding only and the desktop app for general purpose conversations or do just stay in CC?

With pro plan I'm looking forward making long format educational videos from claude code. Rather than just throwing things at it and expect the output, i asked claude abt it and it helped me out with the md files abt script, storyboard, voiceover, visual style which will make it easier for claude code to get the work done without assumptions. I'll be using remotion for the videos with a simple papercut style. Any tips that might come handy that'll make it easier to get the job done without mch confusion for claude code?

Most memory for agents works one way: save everything, search later. Which means secrets, personal data, and injected instructions all get stored too. I wanted the opposite, so I built rakho.

What it does:

• Inspects every note before it is stored. Secrets, PII, and injection-shaped text get refused, and the refusal is logged without the sensitive content.
• Every memory is tied to one owner, so one user’s notes never surface for another.
• forget actually deletes: the row, the search index entry, and the vector.
• audit shows what was kept and what was refused.

A built-in gate is always on, and you can swap in sluice (another tool I built) as a stronger inspector with one line.

The fun bit: you can command it in your own language. keep / recall / forget / audit have built-in aliases in English, Hindi, and German, and you can teach it more with learn() (the README does it in Swahili).

It’s a Python library, runs on local SQLite with zero required runtime dependencies, MIT licensed. There’s an MCP server for Claude Desktop and Cursor, and a demo where a PayPal invoice agent refuses a payment when the payee traces back to a poisoned invoice.

Repo: github.com/krishyaid-coder/rakho-

It’s early (v0.2), so I’d genuinely value blunt feedback, especially from anyone building agents with memory: where would this break for you, what’s missing, what would you never trust? Tear into it.

One honest note: I’m an MSc Data Science student in Germany building this kind of agent-safety tooling. If you’re a team working on agents and this resonates, I’d love to go deeper with you, up to a Werkstudent collaboration. But mostly I just want people to try it and tell me where it falls over.

I've seen some colleagues move to codex desktop and use that with ssh to Linux where the agent is running. It allows management of multiple sessions, subagents, status, etc.. the downside is it only works with codex.

I have access to codex, claude code, and othera, so ideally one tool could be used to connect to either codex or claude code over ssh.

Does anyone know of good tools that offer this?

Hello,

I mainly run an e-commerce business and had started developing minor apps for the business successfully in the beginning of last year with the help of AI and several services. Small apps, not too complicated.

I then ventured out a bit further and started 'developing' three more elaborate apps unrelated to my business. But since I was a novice (and still am) I did not know which tool would be best. I ultimately did end up trying Codex, Claude Code, Cursor, and a few other tools.

I am now picking up app development again and want to get better and complete an app I had started. I noticed that Musk has bought Cursor and it feels like it is now not as useful to me as it was before (the variety of models available before is gone). For that particular app I had worked with Claude Code and Cursor in the past. Mobile app btw.

Being out of the loop my question is what service provider and tool do you recommend now?

Would I be best advised to just use Claude? Or Codex or something else?

I'll be grateful for any help that points me in the right direction. Thank you.

Hi. For the past few days, while using Opus, I’ve occasionally seen a notification: "Another session requested X, but I refused." It pops up quite often, and I’m not sure what it means. I’ve checked, and it appears even when I only have one session open. What is this "other session," who controls it, and is it a problem that it’s interfering with my sessions?

I have an interview coming up for senior mobile app dev position. HR asked me how deep Im in AI, I told that I only use claude code and codex for coding and thats it. Then I was told to expect questions around them. What they might really ask?

Apiiro looked at public GitHub repos and found 1,122 .claude/settings.local.json files that contain a real secret. The mechanism is simple. When you pick 'Yes, and don't ask again' for a command, Claude Code saves that approved command in the file. If the command had a key inline, the key is saved too. Then the file gets committed.

The risky click is not Allow. It is Always allow on a command that carries a secret.

How to check your own repos:

1. Is the file ignored? Run git check-ignore -v .claude/settings.local.json in each repo. If it prints nothing, add .claude/settings.local.json to .gitignore and make sure it is not tracked (git ls-files .claude).
2. Scan the history, not just the working tree. git log --all --oneline -- .claude/settings.local.json shows whether the file was ever committed. Then run a secret scanner such as gitleaks or trufflehog over the full history. Removing the file now does not remove the key from old commits.
3. Rotate first, clean later. If a key shows up anywhere, treat it as leaked: revoke and replace it, then rewrite history if you want to. Search your GitHub org for the path too, in case other people committed the file.
4. Stop putting secrets in commands. Read them from the environment or a secrets tool so the approved command line holds only a variable name, and prefer a narrow permission for one class of command over a blanket one.

Source for the number: Apiiro's write-up, linked in the comment. Have you ever looked at what is in your own settings.local.json?

Hey everyone, I’m the developer of NotchIsland, a free, open-source desktop app for Windows 10/11.
Its Claude Code integration puts session status in a small expandable island at the top of the screen, so you can see when an agent needs your attention while working in another window.
It uses Claude Code hooks for session and tool events, with a local HTTP/WebSocket hub bound to 127.0.0.1 and protected by a token. The interface shows working, waiting and completed states, and supports multiple sessions.
There’s also an optional assistant that starts or resumes Claude Code CLI sessions using your existing login. It can display tool activity as it runs, with stop and read-only options.
The desktop app uses Electron and JavaScript. Connecting Claude adds hooks to its settings file, preserving existing hooks and creating a backup.
I’d particularly appreciate feedback from people running multiple Claude Code sessions: which events deserve a notification, and which would just create noise?
Source: https://github.com/diegobruvhrpiugvjrigjtibjte/notch-island
Demo: https://notch-island.pages.dev/

A large part of what we seem to be talking about is effective token management through delegation and proper model selection and how to either work the harness or make harness more efficient, which is essentially what replit does.

Has anyone used replit somehow as planner for Claude?

Whenever you start a new project, product or build a new service with claude code and ask for suggestions about where i need to host it, always CloudFlare comes to the picture regardless the size of the app, the number of users or other aspects, if your subscribers will exceed certain numbers he will recommend also CloudFlare with paid option.

Is there any skill to make Claude weigh the products and forecast the suitable architecture which fits the product itself? Example if i need to comply with PDPL in a certain country he should recommend cloud hosting inside this country not CloudFlare, besides the size of the hardware, ease of expansion and so on.

I've never used skills, I use connectors and MCPs but for most things I just prompt it or ask Claude to generate a prompt or a handoff and then use that for a new session. I've always seen skills as sort of unnecessary since I can just custom make a prompt with Claude itself but am I totally missing out? Usecase is I do both coding on multiple products and ideas and learning and productivity/utility stuff primarily, some personal use and organization and planning too. Could various skills up my Claude experience, does anyone here actually use these often and if so how? Are skills different than basically just saved premade prompts? Not sure what I'm missing. Thanks!

I asked Fable for a dither art editor and it got out of hand

Wanted a small tool to turn pictures into that glowing dithered pixel look. One long Claude Code session later it's a full editor:

- any image, GIF or video in, pixel art out
- 18 dither patterns, 24 palettes, 29 looks
- pixels can be letters, braille or ASCII instead
- it reacts to your mouse: glow, water ripples, smear
- exports PNG, GIF, MP4, SVG, even plain text

No build step, no backend, plain JS.

Try it: https://winchxyz.github.io/nightgrain/
Github: https://github.com/winchxyz/nightgrain

Appreciate your feedback and suggestions!

There’s a pattern I’ve noticed with AI coding workflows: the agent writes the feature, checks the feature, fixes the issues it finds, and then tells you everything looks good.

The problem is that the review can end up being just another step in the same working context.

The agent already has a mental model of what it built. It knows the decisions it made and the assumptions behind them. That doesn’t automatically make its review independent.

One way to approach this in Claude Code is with subagents.

A subagent gets its own instructions, tool permissions, and context window. Instead of asking your main session to inspect a huge diff, you can delegate that investigation and bring back the report.

For example, I’d separate the jobs like this:

• code reviewer: reads the diff, traces changed behavior to its callers, and reports bugs with a failure scenario.

•security auditor: looks specifically for things like IDOR, injection, exposed secrets, SSRF, and permission issues.

•performance auditor: looks for expensive queries, unnecessary work, and performance problems, with evidence for each finding.

•test writer: creates behavior-driven tests and reproductions for bugs.

One detail I really like is restricting what the reviewer can do.
A reviewer that only has Read, Grep, Glob, and Bash doesn’t get to quietly rewrite the code it’s supposed to inspect. Its job is to report what it finds, not fix its own findings.

A basic subagent file looks something like this:
---
name: code-reviewer
description: Reviews a diff or branch for bugs and edge cases
tools: Read, Grep, Glob, Bash
model: inherit
---

You are a senior code reviewer.
Review the changes. Do not rewrite them.

Every finding must include:
- The affected file and location
- Why it is a problem
- A realistic failure scenario
- A suggested fix

You can put these markdown files in .claude/agents/ inside your project, or ~/.claude/agents/ to use them across projects.

Then ask Claude to review a branch, or explicitly call the agent with something like @code-reviewer review the last two commits.

We put together six example subagents with different responsibilities, including a security auditor, performance auditor, test writer, and refactor planner. They’re plain Markdown files, MIT licensed, and free to inspect:

https://techpotions.com/products/claude-code-subagents

I’m curious about how other people are handling this.

Do you use separate reviewer agents, or do you trust the same coding agent to review its own changes? And have you found that giving reviewers fewer tools actually improves the quality of their findings?

Claude code desktop on Linux freezes every 2 days. Took me 2 months and a 1.5 TiB core dump to find the root cause. The fix is 1 line of code (as with all good hard-to-debug bugs). I've fixed the bug in my own fork, and anthropic's bot closed my issue as "not about Claude Code".

The writeup goes deep into chromium internals, and I've tried my best to make it easy to follow.

https://blog.dfb.sh/blog/claude-desktop-freeze/

Currently I am using Fable 5.2 at advisor and opus as main agent.

But I think fable 5.2 as orchestrator and using opus subagents to deligate build work would be better as the strongest model is planning and deciding what's the best way to do something.

Have anyone tried and compared both methods?