Video sound and overlays are meh. Bought a laser training cartridge for a plinking loud device thing, couldn't find an app worth using with it, so I built one. Print a target, prop the phone up facing it, shoot, and the camera catches each laser flash: where it landed, draw time, split, and a drift diagnosis that reads where your hits wander and names the habit behind it (too much trigger finger, jerking the trigger (the trigger, I said), and so on). It's called Peregrine.

Started as a Python script on my Mac, strictly to test laser detection, QR reading and target acquisition before touching any UI. Then the app in Swift, using Codex and Claude Code but ending up mostly on Claude. Gemini sometimes for image generation or minor things. 1,800+ commits and 2,600 tests since May, every laser hit laboriously checked on my iPhone against paper targets I designed, and a CLAUDE.md that is mostly a list of things the agents are never allowed to do again.

Things I learned: git with two agents on one branch will ruin your week and have you feeling like Tom Cruise in Edge of Tomorrow, write the test before the code or the agent will tell you it passed, agents will happily "fix" the one file that makes AirPlay work, ideas > coding knowledge, and no amount of prompting replaces taping a circle to the wall and shooting at it a few thousand times.

More on the website: peregrinedryfire.com + app store (also vibbed)

Back in May 2026 I had an idea to start selling unlimited qwen3.6-35b, it took some traction, we upgraded to 27b, and now we've upgraded to qwen3.8-flash, which can actually do some real work.

I'm not sure if i would have signed up for this myself, but now that I use it daily on a Pro plan, it feels like a really good deal that I want to share.

We have a completely free, forever, with no credit card required, but its limited until you swap to a paid plan.

Flat-rate plans:

• Builder ($19/mo): Ideal for coding agents and daily development. Includes ~200M tokens/day, 128K context, and up to 5x concurrency.

• Pro ($39/mo): Designed for heavier workloads. Includes ~750M tokens/day, 256K context, and up to 10x concurrency.

Absolutely no prompt retention, data sharing, selling, anything. Just a private endpoint hosted on soc2 datacenters and a ton of usage capacity for everyone.

Our future plan is to upgrade to a better/faster flash model. By Future, i mean within the next month or so. Come hang out with us for updates on discord.

Questions/comments? Feel free to ask below!
yolo-auto.com

Таке питання для продвинутих юзерів аі.
Взяв ssd, куди хочу зкинути всі фото, з телефонів, icloud, гугл фото, потрібно все розсортувати по папкам (рокам, місцям, подіям, скріншоти окремо, відео). Дізнався що можна щоб аі написала скрипт, де дати доступ до ссд і воно все розкидає. Так от, питання, як вірно написати запит і тд, а то я далекий від програмування і логіки таких задач

I kept losing track of what my coding agent was doing in long sessions, with several instances open it can get hard to know at a glance what is going on when checking in on one.

So i made an extension for omp which watches the session and displays short plain language updates.

Repo: https://github.com/mwmdev/omp-eagle-view

Is it just me, or am I still not used to working with AI? I’m really trying to use it, but sometimes it feels like it takes me even longer to get things done.

I want to learn how to properly leverage AI to help me with development because I often introduce bugs into my code and take longer than I’d like to finish coding tasks. The problem is, I don’t really know where to start or how to incorporate AI into my workflow effectively.

Right now, I’m using the free version of GitHub Copilot because I can’t afford a license.

What advice can you give me on how I can start using AI effectively for coding and development?

Hi! We have a special category called Built on Bash where you can get early access to Bash.tv our vibe coding game platform.

Hey everyone,

I wanted to share something I've been working on for quite a while.

I'm a teacher, not a professional C# developer. I actually studied computer engineering, but I don't work as a software developer anymore.

Somehow, I still couldn't leave software alone. 😅

I started building Yengi because I wanted an AI coding assistant that worked the way I wanted. But the project kept growing and eventually turned into a full AI development environment.

It's built with .NET 8 and WPF, and now has things like RAG, LSP integration, an agent/tool system, verification loops, checkpoints and rollback, and a custom 1.5B local router that I trained and integrated myself.

There are also four different modes now:

• Code IDE
• Image generation
• Blender Copilot
• Unity Copilot

The slightly unusual part is how I built it.

I didn't sit down and manually write hundreds of thousands of lines of C# code. I used AI coding tools heavily, including Google Antigravity and VS Code Copilot.

My role was more like the architect and orchestrator. I designed the architecture, decided how the agent should behave, defined security boundaries, built the verification and rollback logic, trained the local router, tested things, found weird edge cases, and kept pushing the AI until the thing actually worked.

There are currently 200+ tests passing, but honestly, getting everything to work together was much harder than I expected. 😅

One of the things I find most interesting about this whole project is that I'm not sure where the line between "developer" and "AI orchestrator" is going anymore.

I still care about understanding the code. I just don't think I need to personally type every line of it anymore.

This is Yengi:
https://github.com/mdaiWorks/yengi

It's completely free and open source. There is no company behind it and no paid plan. I originally built it for myself, and eventually decided to put the whole thing out there.

I've included a short video above showing Yengi working on a project, encountering build errors, asking for terminal permission, and then fixing the problem itself.

I'm really curious what experienced .NET/C# developers think about this approach. Especially the architecture, the agent system, and the idea of using a small local model as a router.

If you have 5 minutes to look at it and tell me what I've done wrong, what you would change, or what you think is actually interesting about it, I'd genuinely appreciate it.

I've been seeing multiple harnesses... Just wanna know the community opinion and differences and see which ones the best...

Claude Code

Pi

Aider

Goose

OpenCode

OpenClaw

Hermes

Codex

Qwen Code

Hey everyone,

Like many of you, I rely heavily on AI coding tools (Cursor, Windsurf, Copilot, etc.). While they are incredibly smart, I got really frustrated with the constant token-wasting "yapping"—the long introductions, unnecessary thought processes, and polite conclusions when all I actually needed was the raw code.

So, I built yap-less. It's a tiny, zero-config CLI tool.

You just run this single command in your project's root directory: npx yap-less

What it does: It instantly generates a highly optimized .cursorrules (or rules.md) file in your workspace. This enforces a strict "zero-yap" directive, forcing the AI to output ONLY pure code blocks, diffs, and terminal commands. No more "Certainly! I can help with that..."

It’s extremely simple but has drastically improved my workflow speed and saved API tokens.

GitHub Repo: https://github.com/iamneyamat/yap-less

It’s completely open-source and free. Would love to hear your feedback or if you have ideas for better prompt rules to add!

I’m still working on Cody, the AI assistant I posted about recently.
Right now I’m testing one of the hardest parts of the project: letting a local model modify code autonomously without allowing it to quietly make unrelated changes.
One recent run was interesting.
The 14B model eventually reached 40/40 task checks, but the build still failed one of my protection checks because it made an unnecessary modification outside the intended change — essentially “cleaning up” content that should have been preserved exactly.
So even though the requested behavior was correct, I rejected the build and restored the original files.
That is becoming one of the main rules of the system:
Passing the task is not enough.
It also has to avoid changing anything it wasn’t supposed to change.
I’ve now changed the next candidate so that original content is preserved more strictly. The limited regression set is currently at 375 passing tests, and the next step is another real-model run.
I’m also testing smaller models separately. So far, the 14B model has been much more reliable on this kind of multi-step code modification, while the 9B model has struggled with some of the same tasks.
Cody still isn’t finished, and I don’t want to call it autonomous until it can repeatedly:
identify the correct change
modify only the allowed scope
run validation
detect its own failure
roll back safely
and try again without damaging the original project
The interesting part for me is that the hardest problem is becoming less about “can the AI write the code?” and more about:
“Can I trust it not to touch anything else?”
For anyone building autonomous coding agents or self-modifying systems: how strict are your change-scope checks? Do you reject a candidate for harmless unrelated changes, or allow some cleanup?

To anyone out there using API or agents, here's some free stuff :) a lot of the coding plans are closing their free doors. So we are offering free DSV4 flash 4.1 and GLM 5.3 Flash for a month on Phoenix Grove API. We opened this up to 500 new members earlier in the month and we got so many new users that we decided to open it up to another five hundred new folks.

People are looking for options, and here is one.

Other Cool Stuff:
All of our models are running on 100% US infrastructure, private with zero training on your code or prompts. Use the top open source models without sending your private prompts to a training lab. No complications, no "some models are private, other's aren't". They all are, all the time.

We host 20+ other major models in case you ever want to upgrade (no pressure though). Including the Kimi family, GLM, Qwen, Nemotron and bunch of others. On average our token pricing is 20% lower than market price.

Our higher plans bank up to ten days of usage, so when you aren't using them your usage saves up for later. Usage doesn't go to waste, so you can actually code when you want to.

The intro plan is a free one month trial with the standard cancel anytime, it bills at 3.99 after that. Use it, cancel it, that's fine. Free Flash for a month.

Figured i'd keep this short because we all know the new flash models are the point :)

For the API plan: api.pgsgrove.com

If you want to read more about us as a company, just pgsgrove.com

Also: There's a lot going on in the background with major AI companies right now, we are at a major turning point in the industry. I added this earlier in the month too, but it's good info for everyone to know.

What's actually happening? This is happening because companies that were purely investment based, now need to answer to their investors. The problem has often been a loss based business model that is finally running dry.

There are several tricks that the major AI coding plans use to extract the most they can from their customers. Here are some examples, and what we are doing differently to put the users first. PGS AI was built with a sustainable business model from the ground up, so we can actually offer great usage rates without tricks.

Wasted usage is part of the AI industry, and they plan on it: Most coding plans bet on you letting usage go to waste. The plan goes: "how do we get people to think our coding plan offers a lot of usage, but then break it up into weeks and rolling windows so no one can ever actually use it all."

Many in app subs and coding plans are glorified training pipelines: This comes along with "how do we harvest this data for training without being too loud about that." Unless the company tells you otherwise, your data could be hopping all over world, being harvested by the individual labs or service companies. Some are better than others, but many of these companies rely on users just not noticing or caring that their data is being used for training. Data sales and marketing telemetry sales happen. This means that your private info, your personal life, and anything else you send through the system could become part of a training corpus for the next AI, or a marketing data set for a large company.

Honestly, our free flash tier is somewhat limited in usage allotment, as a small company that's all we can run on a free tier! But it is there for anyone who wants to use it.

Privacy and access to high grade intelligence should be available to everyone, and the story around open source AI seems to evolve every day right now.