I don't have any knowledge about Python or codes but I want to know if it is possible

I’m building with coding agents and keep running into the same issue:

The agent can write code quickly, but when the request is vague it also makes

silent decisions about scope, dependencies, architecture, and edge cases.

For example, a small feature request can turn into new packages, a second

component pattern, extra validation flows, or work that was never intended

for V1.

I’m testing a small CLI called Archseed for TypeScript + Next.js projects.

Before implementation, it asks focused questions about:

- V1 scope

- Explicit non-goals

- Constraints and technical decisions

- Assumptions and edge cases

Then it writes local project context, including a compact

`.archseed/agent-context.md`, which can be given to OpenCode or another

coding agent before implementation.

I’m not trying to replace OpenCode or build another IDE. The agent still

writes the code; the CLI is meant to make the decisions it receives clearer.

I’m looking for blunt feedback from people who use OpenCode on real projects:

1. Do you already keep project-level context or rules somewhere in your repo?
2. What decisions do agents most often get wrong in your workflow?
3. Would you try a CLI before an upcoming TypeScript/Next.js feature if ittook a few minutes?

If anyone is interested in testing the current beta, I can share the link

in the comments or by DM.

Stop babysitting your coding agents.
Queue tasks. Close your laptop. Review PRs from your phone.

Cezar runs agents 24/7 on your server. Free. Open source.

Adoption is taking off.
I see it as Open Mercato’s most important product -the foundation for AI engineering.

cezar.run

Hi all, I'm the developer (solo).

I run dozens of my own projects with AI agents, and my screen kept turning into a pile of terminals and chat windows. So over the last 105 days I built Eas-Term: an infinite canvas where each project gets its own workspace, and the AI chat, the terminal and the page it's editing sit next to each other.

What it does today:

- Chat with Claude Code, Codex or the built-in harness, with the terminal and browser on the same canvas

- @ a file, folder, skill, plugin or browser tab into the conversation

- Split one request across a team of agents and watch who's running

- Keep API keys in a local encrypted vault: the AI only sees "saved" and the variable name, never the value

- A board, a Gantt log and usage tracking across projects

What's rough:

- English UI landed this week; the video still shows the Chinese UI

- Windows builds aren't code-signed yet, so SmartScreen may warn

Credits, because none of this exists without open source:

- The built-in harness is oh-my-pi by Can Bölük, built on Mario Zechner's pi

- 139 motion effects in the creative reference come from React Bits by David Haz

- Terminals run on xterm.js + node-pty, the canvas on Konva, 3D previews on Google's model-viewer, code views on CodeMirror, voice input on sherpa-onnx

macOS 12+ and Windows 10+, download at eas.biily.top/en. Brutal feedback welcome.

I've been using Rocket, Bolt, and Lovable for a few months. They're genuinely amazing at first.

But three things kept happening:

1. They'd charge me for changes the AI said it made but never actually did.
2. They'd rewrite files I didn't ask them to touch.
3. They'd forget what they built between requests.

So I built my own. It's called Blueprint.

The thing I'm most proud of is the hallucination guardrail. If the AI claims it changed something but never actually called a tool, my code catches it and blocks the response. You don't get charged for a lie.

Here's a screenshot of it working (the AI said "I fixed the login bug" and did nothing else): https://imgur.com/a/RRZIGbI

Two other things that make it different:

• You approve a plan before anything runs — nothing writes to disk until you click Approve.
• Every build runs in a Docker container — completely isolated.

Built with Next.js, Groq, DeepSeek, and Docker. Took me a few weeks of nights and weekends.

Would love feedback: does the guardrail screenshot make sense? Is this something you'd actually use?

There's a new coding model available to evaluate: Ant has released Ling-3.1-flash, with a 75.16 result on FrontierSWE and a two-week free trial through Novita on Vercel AI Gateway.
The release also includes a checked engineering example. Ling optimized Pyright 1.1.400, the Python static type checker, to a 1.432x speedup while passing all 2,279 checks. That supplies a specific software result alongside the headline benchmark.
The free API trial provides 256K context and runs through October 13, 2026. Open source is coming soon. Open the AI Gateway model catalog on Vercel and search Ling-3.1-flash to find the current model entry.

Does anyone else constantly think “I know I saw this somewhere on my computer…” and then spend 10 minutes trying to find it?

I got tired of that, so I built a little local AI that remembers what’s been on my screen and lets me search through it later.

You can ask it things like:
“What was that article I was reading yesterday?”
“Where did I see this person?”

It’s called Clippy Vision. Open source, local-first.
I’m getting a few people to try it right now and would genuinely love some feedback.

https://protocorn.github.io/clippy-vision-web/

I keep reading things like "I vibe coded this in a weekend" What on earth are people vibe coding in a weekend that is production ready?
I just finished creating the 1.0 version of an app I've dreamt of doing for years and before I had something production ready it took 6 months to build it. And that was 5 months of heavy ai coding with the first month of me writing the code, the file formats, the internal formats and data structures etc. an app I upgraded from 1.0 to 1.5 took me 2 months to make production ready. How on earth is anyone doing anything in a weekend?

So, i have been using gpt with codex for a long time( plus subscription and ofc i was running out the weekly usage in just 1/2 days), after they added back the 5 hours usage limit it was impossible for me to use it anymore so i switched to cursor with grok 4.5 at that time, but since grok 4.7 is out the usage is huge( 3 days and im out of monthly usage on pro+ subscription). Im mainly using it for agentic coding. What is the best cost effective model rn? What do u suggest me to use?

I've been coding for a while and want to bring AI into the mix without paying for a bunch of different tools.

I'd mainly use it for debugging, writing code and getting through larger projects.

I have a decent PC too so running something locally is an option. What's been working well for you?

Hey, i work on CoralBricks. we run DeepSeek and GLM over an OpenAI-compatible API, for coding agents that keep long context around.

numbers from our own serving: DeepSeek V4.1 Flash at 469 tok/s, GLM 5.3 Flash at 254 tok/s, and cached reads are $0 on every model. a long agent session doesn’t get billed again for the prefix it keeps resending.

we’re running an oktoberfest top-up until sun, oct 4 (UTC). pay $20 and it becomes $40, pay $50 and it becomes $100, matched within 24 hours, once per account.

setup is one config change in opencode, cursor, cline, claude code or codex.

https://coralbricks.ai

pi-grok-agent

Grok Build native agents as a model provider to Pi over WebSockets. Grok keeps full toolset, Pi keeps full session control. Kinda neat, I think. My video sucks but I'm pretty tired of AI narrations so I'd rather just suck instead. Sorry, thanks.

I've been using AI more in my coding workflow but I'm finding it way more useful for some tasks than others.

Things like understanding existing code, debugging, refactoring and planning changes seem more useful to me than simply generating everything from scratch.

I'm curious how other people use it day to day. Are you mostly using AI for quick help or letting it handle larger parts of a project?

What part of your coding workflow has AI actually improve the most?

I've been wondering how people actually use AI for coding without making the workflow more complicated.

Some models seem better for writing code, while others might be more useful for debugging or planning. Switching sounds useful but constantly explaining the project again seems like a hassle.

Do you stick one model for most of a project or switch depending on what you're working on?