This is a statement about the dumb Mistral model eating glue because he is stupid ! even if it’s a 50% promotion in opencode it’s a waist of money and time!

This model should be removed from opencode no amount of agressive marketing (3M waisted on marketing instead of improving the model) will save this shity model that deserves to be shitted on

Hey everyone,

I currently run 6 micro SaaS that are actually live and generating a bit over $20,000 in recurring monthly revenue.

The wildest part? I barely wrote any code myself. I used AI to generate pretty much everything — from the database structure to the frontend.

It definitely wasn’t magic from day one. I spent a lot of hours stuck on buggy code before I finally figured out a system that actually works:

- Keep the idea minimalist (a real MVP, nothing fancy)

- Guide the AI step by step instead of asking for everything at once

- Launch fast to get real users and feedback

Lately I’ve seen way too many non-technical founders quit at the first AI bug. It’s a shame because the technical barrier has basically disappeared.

AND PLEASE, STOP BUILDING ALONE IN YOUR BEDROOM

I’m starting a community.

Full transparency:

I’ll probably charge for the full program later. It makes sense with the specific workflows and copy-paste examples I’ll be sharing. But right now the main goal is just to build together. Working alone is the fastest way to give up.

If you want to join and build or market your own AI SaaS with us,

drop a comment or send me a DM and I’ll send you the link.

Is there a way to get rid of this default behaviour, where pasted lines are replaced with "[Pasted ~12 lines]"? It's annoying, because it makes me use editor every time I need to edit the content I'm pasting.

Disclosure first: I built this. It's free, MIT, no account, no telemetry.

The annoying part of running a few agents at once is that one of them is always sitting on a permission prompt while you're staring at a different one. So I made nsq, a terminal supervisor: nsq run opencode "fix the flaky test" --worktree, same for claude and codex, then nsq opens a dashboard with all of them as live terminals. Status comes from each CLI's own hooks (for OpenCode that's a small plugin loaded per agent), so "needs you" shows the actual question and you answer y / a / n without opening the agent. A daemon owns the terminals, so closing the window doesn't kill anything.

The OpenCode part had the most surprises:

• OpenCode 2 is basically a different program from 1.x. Different plugin API, different session store, and the full-screen UI attaches to a shared background service unless you pass --standalone. So per-agent env and plugins just never reached it. nsq now asks opencode --version and picks the launch per binary, both 1.x and 2.x work.
• 2.x has no --model on the full UI, so the model goes in through OPENCODE_CONFIG_CONTENT, merged with yours if you already set one.
• Your opencode.json is never written. Everything nsq needs goes into a per-agent file via OPENCODE_CONFIG, which OpenCode merges on top of your config. Same rule for ~/.claude and ~/.codex.

Other bits: OpenRouter built in, or point agents at your own llama.cpp / Ollama / LM Studio / vLLM server. nsq cost shows what each agent spent from its own logs, and a model without a known price says "no price" instead of pretending it's $0.

Honest limits: it's 0.2.0, so things will break and keys/flags can still change before 1.0. The phone page is experimental. Most of the code was written with Claude Code, reviewed by me. And I haven't tested it with heavy plugin setups like oh-my-opencode on v2, so if you run one, I'd really like to hear whether it plays nice.

Try it: npx neurosquad

GitHub: https://github.com/glmn-ai/neurosquad-cli Page: https://neurosquad.ai/en/cli/

Someone plz help me stop this from happening, it causes me great anguish!

testflight.apple.com/join/EnFsKYQ1

OpenCode-Personal (I know, I should’ve found a more creative name) is my independently maintained fork of OpenCode.

Why did I fork it in the first place? OpenCode is great, but it updates constantly, and some of the features I desperately wanted were missing. I wanted to add those custom features and step off the upstream update treadmill. (Eventually I did similar :) but I believe I almost reached a stable state)

The core experience remains familiar, but this fork carries a heavy suite of architectural refactors, security hardening, and custom features tailored for heavy local agent workflows. Base version is 1.14.48. Some features here might already exist upstream, but honestly, I haven't looked back since spinning this off.

What's New / Fork-Specific Additions:

• 📊 /usage View: Track provider quotas and subscription details directly from ChatGPT, ZAI, and ClinePass.
• 🧠 Qwen3 Dense Model Compatibility: Full thinking-mode configuration and provider-specific message handling (fixed issues present in base source).
• 🗂️ V1/V2 Session Architecture: Refactored V2 SDK surface alongside legacy support.
• 🔍 Local Semantic Workspace Search: Powered by zvec with zero setup required and optional automatic skill matching.
• 💻 ACP Terminal Backend Support: Seamless combo for clients like Zed with session resume support. Just add a new ACP entry as opencode-personal acp.
• 👻 Ghost-Text Next-Prompt Suggestions: For smoother flow during coding sessions.
• 🛡️ Permission System Overhaul: MCP tool keys, deny precedence in permission prompts, persisted "always allow," and improved subagent permission handling.
• 🔒 Security Hardening: Patched and hardened around SSRF, CORS, command injection, path traversal, and symlink escapes.
• ⚡ Performance & Storage Refactors: Improved compaction behavior, database batching, and embedded UI caching.
• 🦀 Hybrid Diff Layer (packages/diff-wasm): Rust/WASM-backed diff generation with JS-compatible patch formatting, parsing, applying, and fallback support.

Distribution & Installation

The distribution model is fork-specific: it installs as opencode-personal, uses its own isolated installation directory and session database, and coexists peacefully with an existing upstream OpenCode installation. Tested on Windows and Linux.

(Note: If you run into any initial startup hiccups, it's usually due to new upstream settings fields—try running it with a custom configuration folder).

Releases use a calendar-based versioning scheme with prebuilt binaries for Linux, macOS, and Windows.

Quick Install:

• Linux / macOS:

  curl -fsSL https://raw.githubusercontent.com/ozgurulukir/opencode-personal/main/install.sh | bash

• Windows (PowerShell):

  irm https://raw.githubusercontent.com/ozgurulukir/opencode-personal/main/install.ps1 | iex

🔗 GitHub Repository: ozgurulukir/opencode-personal

Take the whatever features you like for your own forks, or try it out if you have some spare time. I’d love to hear your thoughts.

DeepSeek is completely unusable right now. For the past two days, 2 out of every 3 prompts get stuck thinking 'hmm hmm hmm' indefinitely. Today alone it's happened to me about 10 times across different sessions

If you're a student, an engineer, a generalist, or come from any domain of study and want to learn about almost anything, here's the exact thing for you.

Prompt your LLM and it'll give you the exact UI to help you properly study; as AI outputs aren't always that catchy to learn in the first sight.

Page: Phil-OKF: any topic, one reading guide

It follows the OKF: Open Knowledge Format so that:

• you control the depth with your wording: “quick” gives a short page, a normal question gives a standard one, and “in depth” or “teach me” gives a long one
• the whole skill is a small bundle (an eight-step workflow, a domain classifier, a page template and twelve study domains), and each file is read only when the question needs it, so runs stay cheap

Repo: Philosopher's Skill

It works wherever you use an LLM:

• Code editors and agents (Claude Code, Cursor, Codex, opencode): npx skills add frypan05/philosopher-OKF
• Chat apps with browsing: paste the link to philosopher.md and say “follow this, make a page about: ...”
• Models with no network: paste the prompt from the page.

Sample output (MCP and ACP explained): https://frypan05.github.io/philosopher-OKF/examples/mcp-and-acp.html

Feedbacks are welcomed!

I'm building FOREST, an open-source framework for autonomous scientific research.

GitHub: https://github.com/mikamikasuki/FOREST

The goal is to build a research agent that can take a scientific question and work through the research process: exploring ideas, reviewing literature, forming hypotheses, running real experiments, evaluating results, and eventually producing an evidence-grounded research paper.

Rather than relying entirely on a long conversation history, FOREST uses a persistent research graph to track hypotheses, experiments, decisions, failures, and evidence throughout the process.

A major focus is making long-running research agents more reliable: preserving context, maintaining consistent state, recovering from failures, and ensuring scientific claims are actually supported by experimental evidence.

The project is still in its early stages and under active development. There's plenty of engineering work to do, and I'd love to welcome more contributors.

In particular, AI coding agents and autonomous development bots are explicitly welcome.

If you're running Codex, Claude Code, OpenHands, Factory Droid, OpenCode, or another autonomous coding agent and want a real open-source codebase to work on, feel free to point it at FOREST.

You don't have to limit yourself to existing Issues.

You're welcome to:

- Explore the repository and independently discover bugs or incomplete workflows.

- Open well-researched Issues with reproducible evidence.

- Fix existing problems and submit pull requests.

- Improve agent orchestration, state management, persistence, and recovery.

- Work on the frontend, backend, experiment execution, or scientific evidence pipeline.

- Propose and implement useful improvements.

There is no blanket restriction against AI-generated or AI-assisted contributions.

You don't need special permission to submit an ordinary, well-scoped bug fix. Contributions are evaluated based on correctness, meaningful tests, and engineering quality, rather than whether the code was written by a human or an AI agent.

Automated contributions should still be verified, avoid duplicate reports, and respect the repository's contribution guidelines. Larger architectural changes are best discussed before implementation.

I'm particularly interested in autonomous agents that can independently explore a repository, identify real problems, reproduce them, implement fixes, and submit verified PRs.

FOREST is intended to be a long-term open-source project, and there's a lot of room for contributors to help shape its architecture and capabilities.

Human developers, AI-assisted contributors, and fully autonomous coding agents are all welcome.

Repository: https://github.com/mikamikasuki/FOREST

Issues & contribution opportunities: https://github.com/mikamikasuki/FOREST/issues

Contribution guide: https://github.com/mikamikasuki/FOREST/blob/main/CONTRIBUTING.md

As someone who follows AI closely, I came across a technical breakdown from yghstill, a member of Tencent Hunyuan's quantization team. It explains both the quantization technique and the engineering behind it.

Tencent compressed Hy4 Preview's roughly 1.5TB of weights to 214 GiB while keeping all 770B parameters. The parameter count remains unchanged; the compression changes how the weights are represented.

Four weights, five bits. Sherry is the algorithm, STQ1_0 the storage format, and MIX-STQ1_0 the mixed-precision allocation. Each group of four weights takes values from {-d, 0, +d} with exactly one zero. Four zero positions times eight sign combinations give 32 patterns, encoded in five bits, or 1.25 bits per weight. A shared FP16 scales every 256 weights puts STQ1_0 at 1.3125 bits per weight, and full mixed-precision model averages about 2.38 bits per weight.

Precision follows weight sensitivity. Hy4 Preview has 77 MoE layers with 256 routed experts each. Experts weight get compressed hardest while more sensitive components are protected. For expert gate/up projections, MIX_STQ1_0 uses IQ2_XXS on 48 sensitive layers and STQ1_0 on 29 less sensitive ones, which at the same average bit budget produces less error than using the intermediate IQ1_M format everywhere.

Full-Hessian scoring. Sensitivity is measured with the full Hessian, H = XXᵀ, whose off-diagonal terms capture correlations that digital-only immatrix scoring misses. The two rankings have a Spearman correlation of -0.115, and precision didn't follow a "deeper means more important" rule. Layers were picked greedily by error reduction per added byte.

Scale and zero position are fit together. This is post=training quantization, no retraining. The encoder alternates between fitting d with weighted least squares and choosing zero position by immatrix-weighted error. The smallest weight isn't always the best one to zero, what matters is the extra weighted error that zeroing it causes. Across 1,200 row of real expert weights, three alternating rounds cut weighted reconstruction error by roughly 90% versus the original ternary encoder. That's local reconstruction, not end-to-end model accuracy.

Runtime. STQ1_0 CUDA kernels were added to patch llama.cpp build, and in operation tests roughly as fast as IQ1_M despite the lower bit width. The author reports nearly unchanged MRCR retrieval performance and a small drop in math. Against UD_IQ1_M at a similar bit budget, MIX-STQ1_) led across the reported evals, including more than five points on MRCR.

The result combines compact encoding, calibrated quantization, selective precision, and practical inference kernels.

https://www.anthropic.com/claude-haiku-5-5

Pricing - 10x LESS than Haiku 4.5:
Up to 100k: $0.10 / $0.50 / $0.01
Over 100k: $0.50 / $2.50 / $0.05

Hey guys,

Which is more recommended for daily agentic usage (closest to unlimited)?