... open-source CLI tool that automatically generates clean, conventional commit messages from your git diff. Stop wasting time writing commit messages and focus on coding. Features: Reads git diff and generates commit messages automatically Supports DeepSeek, OpenAI, and Ollama (local models) Conventional Commits standard Interactive mode edit before commit Custom prompt templates Open core free CLI + Pro features Free version includes all core features. Pro ($29/month) adds team rule checking, PR description generation, changelog generation, and git insights. - ...

... rather than automatically writing everything on their behalf. For this first study, I compared Qwen3 4B, 8B and 14B under the same local conditions: 20 paired writing cases 60 responses generated locally English text correction Explanations requested in French Ollama running on Windows The same evaluation framework for all three models The complete-case correction results were: Qwen3 4B: 18/20 Qwen3 8B: 19/20 Qwen3 14B: 19/20 The result that interested me most was the trade-off between model size ...

... shared conversation and assigns them explicit roles: the Writer drafts, the Critic challenges, and the Judge evaluates. Every execution is traceable as a "run" tracking status, errors, duration, and token usage. Conversations, agents, and the workspace remain local, and Ollama runs without an API key. A note I am not hiding: The code was written entirely by language models. For this project, that is not merely a side effect but central to the concept a tool for multi-agent verification ...

... shipped a batch of thoughtful quality-of-life polish on top landing entirely within Skim's "think for the user, not at them" philosophy. Exactly the kind of contribution this project hoped for. Merci, Gildas. New one-click Ollama, zero trivia ( #16 ) Point the custom (OpenAI-compatible) endpoint at a local Ollama

... contents: redacted blocks are empty by design, so presence is the signal. The SSE test harness counts reasoning reports now, so a spurious one can't hide inside the streamed text. 172 tests , green. Verified live against Ollama (cold and warm start) and Anthropic with a real key recap, chat, mailbox chat, compose, style scan. Nothing changed in answer streaming, tool calls or citations. Grab it here: https://github.com/nikserg/skim/... Comment from Hossein Yazdi(@hosseinyazdi): Nice update. I like ...

... goes on a diet at the same time. Efficiency and choice, in one download. You're welcome. New Bring your own AI endpoint (OpenAI-compatible). A third provider joins Anthropic and OpenRouter: point Skim at any OpenAI-compatible API Ollama, LM Studio, vLLM, your homelab gateway, whatever. Just a Base URL and a Model ID; the API key is optional (leave it blank for local servers that don't want one). No key = no Authorization header, so your Ollama

... beautifully thorough round of AI-layer cleanup ( #18 ) the kind of PR that leaves the codebase tidier than it found it. The slowStart timer, copy-pasted into four components, is now one tiny createSlowStart helper. Same story for Ollama detection createOllamaDetection, deduped out of Settings and Onboarding. Ollama

... charging a monthly fee to categorize my own transactions often by sending my bank data to a cloud API. So I built ClearBudget, a personal budgeting app for Windows that runs its categorization AI entirely on your machine (via Ollama). No cloud, no API costs, no subscription. Import your bank statements, get transactions auto-categorized in seconds, track spending and savings goals and your financial data never leaves your PC. One-time purchase ( 9.99), 7-day free trial, 8 languages ...

... Quick one for the builders here. I run a small business (not a dev shop) and I automated my monthly invoice data-entry with n8n + a local model (Ollama no cloud, no per-call fee). The automation part was easy. The part that almost killed it was subtle: on a blurry or badly scanned invoice, the model would confidently invent a supplier or a total. In accounting, a confident wrong number is worse than no number ...

... open, I started to hesitate. My active vocabulary felt rusty, and I realized I was losing my writing confidence. It brought me to a harsh truth: AI is making us fluent in copy-paste, not in language. The "Pilot" vs. the "Mentor" Problem The real issue with traditional AI tools is that they write instead of you. They take the controls, replace your voice entirely, and fly the plane. Your brain gets zero mental engagement, and you learn absolutely nothing ... ... comparison with an educational feedback panel explaining the stylistic choices. That personal utility eventually evolved into LinguaPilot AI . With the recent v2.6 update, it grew into a flexible hybrid engine (supporting both cloud and 100% offline local models like Ollama

... PostgreSQL, Redis, Mailpit, etc.) in just one click with zero VMs and minimal RAM usage. But in 2026, writing code is changing. Most of us are now building with AI agents like Claude Code, Cursor, or local models via Ollama. That got us thinking: Why hasn't our local dev environment evolved for the AI era? So, we upgraded ServBay to be AI-Native . Here is what we ve added: Built-in MCP Server: Let your coding agents safely inspect ...

... www.producthunt.com/products/ai-... Your turn. Drop: - What you're building - Who it's for - The hardest part you're solving right now Will read everything and upvote what resonates. Comment from Mustafa Arian(@thenameisarian): TAM Network. an AI-native alternative to linkedin. the AI part is the agent identity row. when an AI agent does real work, it gets a row on the receipt next to the human contributors. recruiters see exactly which line item was AI and which was human ... ... live on PH Aug 22, so this thread's been good practice at saying it in one paragraph.) Comment from Demetrio Koro(@aktivist): siOsi upload one selfie and it reads your 12-season color type, undertone, gold vs

... automated loop. Weighted vote shuffle where your upvotes literally change how often a track hits. The Vault system for tracks you want kept but never heard again by accident. Bring your own API key or run fully local via Ollama. Self-hosted. Your data never leaves. No subscription. One time. Ever. Would love your support today and any feedback you've got! More info ...

... setup, not science. That's not an AI problem. That's an infrastructure problem. oneinfer-edge fixes it. Not by reinventing the stack. By orchestrating what already exists into one open source control plane. - Multiple serving libraries. One scan. Ollama, llama.cpp, vLLM, SGLang, TensorRT-LLM, PyTorch, Dynamo. Instead of manually testing each one against your model and hardware, oneinfer-edge evaluates all five simultaneously and tells you exactly which one to use, for local, cloud, or both. Hours of trial ...

... immersive coding experience Whether you want an autonomous coding agent, an AI pair programmer, or simply a smarter development environment, Tollecode adapts to how you work. This is more than an IDE with AI. Using open-source models through Ollama ? Tollecode Desktop is built for that workflow. Run AI locally, keep your code on your machine, reduce API costs, and switch between local and cloud models whenever you need. Your tools should adapt to your workflow not the other ...

... database last month (when Anthropic sent out their email about shifting automated usage to a different monthly budget). Now I'm tweaking them by leaving very consciously chosen runs on Claude (various models) and routing other work to OpenRouter, Ollama Cloud and local LLMs. I've already reduced their cost substantially. Comment from Jiaqi Lu(@jiaqi_lu): Cancelled plan this month. Gonna try Cursor next month. Comment from Jiaqi Lu(@jiaqi_lu): @fmerian  Sure thing, gotta try it next ... ... month to have some thoughts. coz we already paid for github copilot this month Comment from Jiaqi Lu(@jiaqi_lu): @fmerian VS

... Product Hunt! I built FlickClaw because I was spending hours configuring the same AI agents over and over for different tools. Every time I switched between Claude Code, Cursor, Codex, or my local Ollama setup, I'd rewrite the same agent rules from scratch. FlickClaw solves this with pre-packaged agent configs: 150 specialist agents each with identity, operating rules, workflow instructions, quality gates, and output contracts Native exports Claude Code, Cursor, Codex, Aider, Ollama

... VRAM, OS, and serving libraries, gives you a Hardware Ready verdict and full memory breakdown (weights + KV cache + serving overhead). No surprises at runtime. Supports Apple Silicon (M1 to M5), NVIDIA (CUDA), AMD (ROCm), and serving libraries including Ollama, llama.cpp, SGLang, TensorRT-LLM, PyTorch and many more coming. It tells you why something won't work, not just that it won't. CPU support is something we're actively working through and feedback and contributions on that front are very ... ... oneInfer-edge is part of the broader oneinfer.ai inference control plane, a platform built for teams shipping multimodal AI products at scale. oneInfer-edge brings that same infrastructure intelligence to your local machine so self-hosting is a genuine alternative

... VRAM, OS, and serving libraries, gives you a Hardware Ready verdict and full memory breakdown (weights + KV cache + serving overhead). No surprises at runtime. Supports Apple Silicon (M1 to M5), NVIDIA (CUDA), AMD (ROCm), and serving libraries including Ollama, llama.cpp, SGLang, TensorRT-LLM, PyTorch and many more coming. It tells you why something won't work, not just that it won't. CPU support is something we're actively working through and feedback and contributions on that front are very ... ... oneinfer-edge is part of the broader oneinfer.ai inference control plane, a platform built for teams shipping multimodal AI products at scale. oneinfer-edge brings that same infrastructure intelligence to your local machine so self-hosting is a genuine alternative

... from that point every request is logged, costs are tracked, and agent traces are captured automatically. What makes it different from other observability tools out there It supports multiple providers in one place. OpenAI, Anthropic, Gemini, Azure OpenAI, Ollama, LangChain, LlamaIndex, and the Vercel AI SDK all work out of the box. It is fully open-source under the MIT license and self-hostable via Docker. No vendor lock-in, no mandatory cloud plan. The integration takes two lines ...

... takes 40 seconds to respond because nobody told you the serving setup was wrong. And then on the other side, cloud inference costs sneak up on you fast. you're routing everything to the most expensive API because the alternative is wiring up your own fallback logic from scratch. There's no clean middle ground today. no single thing that handles local, cloud, and everything in between without you stitching it together yourself. You might be thinking, what about ollama

... Same search with a structural graph: 3 calls, 60 seconds. Same model. Same question. A few observations from mapping the space: Deployment friction kills adoption. Tools that ship as a single binary get tried. Tools that need Python + Docker + Ollama get bookmarked and forgotten. The scope question is unresolved. Some tools expose 100+ MCP capabilities. Others expose 4. Nobody knows the sweet spot yet. IDE absorption risk. JetBrains ships a built-in MCP server starting with 2025.2. The IDE absorption ...

... need. ArcIDE creates the files, opens the terminal, installs packages, runs builds, catches errors, fixes them, and iterates all without you lifting a finger. Omni-Model Architecture: Not locked into one provider. Switch between Claude, Gemini, DeepSeek, and local Ollama models in a single click. Use the right brain for the right task. Antigravity Logs: Full transparency into what the agent is doing. Every file read, every edit, every command displayed in a clean, real-time professional log feed ...

... Insecure Deserialization, and Remote Code Execution (RCE). System Design Audits: Identifies DRY violations, fragile error handling, and memory bloat. Privacy-First (BYOK): The tool is model-agnostic and OpenAI-API compatible. You can plug in a local instance (like Ollama) for 100% data privacy and zero compute cost. How it Works It generates structured Markdown reports in .pika-reports/, providing a syntax-highlighted, persistent audit trail directly in your project root. Community Feedback Needed I m curious about two things ...

... search. Search. Find code by meaning, not strings. "Payment error handling" finds the right files even if those words don't appear in the source. Think what Graphify or Cursor do, but local and bundled. No API key, no Ollama. Blast. What breaks if I change this? Traces callers, flags affected tests, gives a risk score. Like code-review-graph or Engram, but structural rather than statistical. Conventions. What patterns does this project follow? Only Drift really does this ...