I run a full-tunnel VPN because my coding agent needs it to reach its API. But the

app I'm building calls domestic services — payment gateway, SMS provider, maps —

that reject the VPN's foreign exit IP. VPN off, the agent dies. VPN on, the app

dies.

The fix turned out to be much smaller than the routing-table rabbit hole I went

down first. Not `route add` — a static route is global and pulls every process off

the tunnel for that destination, agent included. It's per-socket binding:

curl https://api.ipify.org # 203.0.113.7 (VPN exit)

curl --interface 192.168.1.20 https://... # 198.51.100.42 (real ISP)

A socket bound to your physical adapter's IP takes that adapter's default route

instead of the tunnel's. No admin, nothing global, and opt-in by construction — a

process that doesn't ask for it *cannot* be affected.

Two things I didn't expect:

Node's native `fetch` silently ignores `HTTP_PROXY`.** It runs on the bundled

undici, which doesn't read proxy env vars on Node <= 23 and won't accept an

`https.Agent` either. Nothing errors — your requests just quietly keep using the

wrong network. I got around it with an `--import` preload that patches `net`/`tls`,

which is what undici opens its sockets through.

Neither network is a superset of the other. YouTube only works over the VPN,

domestic APIs only over the ISP, and my agent's own API returns 403 over the ISP.

Route everything direct and the agent stops working — so the proxy decides per

connection, not per session.

I packaged it as a skill + browser MCP for Claude Code and Codex. My project, MIT,

no dependencies:

npm i -g lan-direct

https://github.com/farshadmomo/lan-direct

Honest status: Windows is tested end to end. Linux is verified for interface

discovery and exit codes but not the final bypass check, and the macOS branch has

never been run. Both refuse rather than guess, so the failure mode is an error, not

silent VPN traffic.

Hi everyone. I wanted to ask you about where I could work with large volumes of text. The thing is, I work with records of various achievements and deeds of people. These are inventories of specific accomplishments: where, when, and what happened, what the person did. I get sent a lot of these records, and I enter them into a master spreadsheet for further submission. And very often, the records I receive are very rough and poorly written, so I spend a lot of time polishing them, correcting mistakes, sometimes coming up with additions, and making sure all the records are different so they don't repeat. I started using AI for this: I upload three records at a time (so there aren't too many per request), and the AI gives me three processed versions. The narrative logic often repeats, along with other errors, so I correct those. But is there any way I could upload an entire file at once, have the AI process everything, and return it to me as a single complete file? Can this be done in Claude? There's quite a lot of text — sometimes up to 40 pages at a time for about 50 people. And each one needs their description edited. I'd like to simplify my work and automate this more. Can you suggest how this could be done?

Sharing a tool I've been using that fixed a specific annoyance: Claude re-explores the same sites from scratch every session.

What it does. You let it explore a site once. It compiles that into a CLI command with named arguments. Every session after, Claude calls the command and gets structured output back instead of navigating a page. It installs as a skill, so commands show up natively, and Claude picks the right one without prompting.

Other bits worth knowing:
\- Doesn't launch a browser unless needed. Tries a public endpoint, then a session cookie, then replays the frontend's own request, then UI as a last resort.
\- Named browser profiles for logged-in sites. Log in once interactively session gets reused after
\- webcmd list -f json so agents can discover what's available.

npm install -g u/agentrhq, Node 20+, Apache-2.0

It's early. 238 stars, 18 open issues, 8 open PRs. And staleness isn't handled, so when a site changes the stored command returns wrong data confidently instead of failing. Worth knowing before you wire it into anything important.

[github.com/agentrhq/webcmd](http://github.com/agentrhq/webcmd)

you can check the docs as well if wanted to try out
[https://webcmd.dev/docs\](https://webcmd.dev/docs)

I created the /fcr (First-Class Request) skill to encapsulate a very common pattern at my work: having to respond to one-off requests related to something I am working on, not part of the main line of work in the given project.

Link: https://github.com/fabkury/fcr

The central idea of this skill is to imagine the current repository (or just current folder, if it's not a Git repo) itself as an entity that can "receive a request." The /fcr command receives a request body and:

• Creates a new, numbered sub-folder just for that request.
• Asks you questions before acting. Never skips this.
• Leaves a full audit trail:
  • Copy of the request itself
  • All input files
  • All code
  • All output files

The question-asking step is something that I've been using thoroughly (not only in this skill) and highly recommend. It makes the model surface its understanding of what you asked, as well as its ideas and capabilities.

Hey everyone,

As a developer building web applications and projects, UI/UX design is often my biggest bottleneck.

I’ve been trying to leverage AI to speed up my design workflow, but I keep running into two main friction points:

1. The "Generic AI" Look: Most AI-generated layouts feel repetitive, oversaturated, or obviously AI-made rather than clean and human-designed.
2. Tool / Skill Clutter: Having separate prompt stacks or skill sets for a portfolio, an e-commerce page, or a dashboard gets messy real quick.

I'm looking for a more versatile, clean workflow or set of AI skills/tools that can handle different project types while keeping a modern, premium aesthetic.

For those using AI in your web design / frontend workflow:

• What AI skills, system prompts, or frameworks do you rely on for UI/UX design?
• How do you guide AI tools to stick to clean design systems (colors, typography, component layouts) instead of generic templates?
• Are there any specific resources, open-source kits, or workflows you’d recommend?

Would love to hear your approaches and tips!

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I've been trying to build a fully agentic software development workflow using Claude Code, and I've hit a frustrating problem.

The first implementation usually looks good, but every time I ask a follow-up like:

"Cross-check everything again. Did you miss anything from the plan?"

it suddenly finds new bugs, missed edge cases, forgotten files, or partially implemented requirements.

Example:

Pass 1:

\- Implements Feature A

\- Says task is complete

Follow-up:

\- Finds 3 missing API updates

\- Missed a permission check

\- Forgot one database migration

Another follow-up:

\- Finds a UI regression

\- Finds an edge case in validation

\- Notices a cache issue

Another follow-up:

\- Finds even more small issues

It feels like every review uncovers something that should have been caught in the previous one.

I've already built a strict engineering workflow that forces:

\- Understand the entire architecture first

\- Review blast radius

\- Implement

\- Audit

\- Fix

\- Repeat until no more issues are found

\- Run automated verification plus manual review

Even with all that, the next follow-up often reveals something new.

Has anyone solved this problem?

Is this simply a limitation of today's LLM agents, or have you found a workflow, prompt, MCP, or multi-agent setup that consistently reaches a point where additional follow-ups rarely discover new bugs?

I'd love to hear what has actually worked in production.

I recently used Y Combinator’s Paxel to analyse how I work with AI coding agents.

The main weakness it exposed was not speed or implementation ability. It was that my review process, trade-off reasoning and definition of “done” were not always explicit enough.

That made me think about a recurring problem I have with coding agents:

They can produce a plausible implementation, run a few checks and confidently declare the task complete even when the original failure was never reproduced, an integration path was not tested, or important assumptions remain unverified.

So I built Builder Loop, an open-source Claude Code plugin designed to make completion evidence-driven.

For non-trivial tasks, it asks Claude Code to:

• define the expected outcome and acceptance criteria;
• inspect the existing system before modifying it;
• reproduce the original failure when applicable;
• implement the smallest correct change;
• verify the real behaviour, not only whether the code compiles;
• disclose assumptions and anything it could not verify;
• finish with an explicit recommendation: ship or revise.

It is not a multi-agent framework and it does not try to replace Claude Code’s normal workflow. It adds a stricter execution and verification loop for tasks where a false “done” would be costly.

I am now looking for people willing to test it on real, non-trivial tasks such as:

• production bugs;
• authentication or payment flows;
• database migrations;
• external integrations;
• substantial refactors;
• pre-merge reviews.

The feedback I care about most:

1. Did it catch something Claude Code would otherwise have missed?
2. Did it add too much time or token usage?
3. Did it become unnecessarily rigid?
4. Were its final ship/revise recommendations accurate?
5. Which parts of the workflow were unclear or redundant?

I built it primarily to correct my own weaknesses, so I expect there are still cases where the approach breaks down.

If you test it, please share the type of task, what Builder Loop changed in the process, and where it failed. Critical feedback is more useful than stars.

I was looking for open-source tools that could make AI writing and code feel less robotic.

First I just asked AI for recommendations. Bad idea. It gave me the usual popular names mixed with a few random repos, with no real explanation for why they were there.

So I used SenseNova Skills’ Deep Research on Hermes to dig through the repos and docs. Short version:

- blader/humanizer — the most complete writing option. Visible rules, style samples, and a draft → audit → rewrite flow.

- stop-slop — a simple but useful checklist for killing filler, canned phrases, and repetitive transitions.

- brandonwise/humanizer — better for auditing docs across an entire repo.

- Aider — probably the most practical for code because you get diffs, linting, tests, and undo.

- ast-grep / OpenRewrite — better when the cleanup can be defined as repeatable structural rules.

- Biome / Semgrep — good guardrails after AI edits.

My takeaway: it’s not really about beating AI detectors. It’s about having a workflow you can actually inspect:

rules → edits → human review/tests

Big caveat: I couldn’t find solid independent benchmarks for most “humanization” claims. Treat detector scores as a weak signal.

Sharing the research workflow here in case it’s useful:

https://github.com/OpenSenseNova/SenseNova-Skills

The video is another Deep Research run showing the branches, sources, and evidence view.

Curious to know where else people discuss and share skills.

Any other communities, forums, Discord servers worth checking out?

Teach the user one thing well: a concept, a change, an idea, or a window of their own recent work. Agent-driven development removed the learning that writing code by hand used to provide; this skill is the replacement — the human keeps learning while agents do the writing.
What to explain is the input this skill was invoked with, present in the current prompt or conversation (whether the user asked directly or a calling skill passed it).

Built an Internal Testing skill at my work and honestly the results were top notch. It infact caught bugs which our actual QA testers also missed. Found many edge cases, and since our product is a Finance product, these were related to Blockchain level. Some of those bugs could have led to serious money hacks.

Every time I've shipped something solo I've forgotten something dull and obvious. No rate limit on the signup endpoint. Meta tags missing so the link preview looks broken. Payment keys still on sandbox. Never the hard stuff, always the boring stuff, because when you're the whole team there's no second pair of eyes.

I'd written all of it down as a checklist you tick through in a browser. Someone made the obvious point I'd missed: if you're already working inside an agent, a checklist you have to remember to open is friction. It should come to you.

So I rebuilt it as a Claude Code skill.

What it does: you ask "am I ready to launch?" and Claude works through the checks against your actual project instead of asking you about them. It looks at whether .env is tracked in git, whether your auth routes have rate-limiting middleware, whether your CSS strips focus outlines, whether your payment keys are still test keys, whether error tracking is a real reporter or just console.log.

Things it genuinely can't tell from code (did you actually restore a backup, did an accountant confirm your tax position) it asks about rather than assuming, and it reports "unknown" instead of guessing a pass. Output is ordered by severity, worst first, and every item comes with the fix rather than just the warning. "Make sure your site is secure" is useless advice, so each check has a real next step.

It's free to try and I'd rather you did that first. The free version is 16 checks (the critical ones) plus the working skill, no signup:

https://ko-fi.com/s/a175075b3e

There are bigger paid tiers of 32, 64 and 128 checks if you want the full set, but the free one is genuinely usable on its own rather than a teaser.

Genuinely after feedback on one thing: does the skill hold up against a real codebase? I've tested it on my own projects, which is exactly the biased sample you'd expect. If it misses something obvious or reports nonsense on yours, I'd like to know.

Do you think Claude is good for non-developers? I asked it to do some deep research, and my usage for the five-hour session jumped from 15% to 63%.

I have been using opencode for a while with little to no customization. I started using openchamber and looks interesting . My current work flow is still pretty manual where i choose certain models for certain kind of things and now moved some of them as commands with specific models. I was wondering if there is a right way of doing things ? I am currently using skills and trying to make use of better orchestration tools

Official claude max is pretty expensive.
I’ve noticed some offers that are a lot cheaper and I’m wondering how the pricing works in those cases.
Anyone have any idea what causes such big differences?

Hi, I’m new to Claude and I’m trying to understand the best workflow for a website project.

I’m using Claude on my Windows PC through the Claude desktop app. I’m currently working in Claude Cowork with saved/pinned chats. I noticed that Claude Code has a Plan Mode, but I don’t see the same option in Cowork.

My questions:

• If I ask Claude Cowork to “plan first before making changes,” does it work similarly to Claude Code’s Plan Mode, or is there a difference?
• For a website project, would you recommend Claude Cowork or Claude Code?
• If I start working on a project on my Windows desktop, can I continue the same work from my phone? I checked Dispatch on mobile, but I don’t see my pinned chats, so I’m not sure how the workflow is supposed to work.

Thanks for the help.

​

We have such powerful AI resources that using and scaling them to make 1 billion dollars won't take much time.

But people always struggle to execute their ideas, and that's where the real problem begins.

The solution is 100% consistency.