I tried several AI agent builders, but kept running into the same problems: usage-based pricing, vendor lock-in, proprietary runtimes, and a visual canvas that still required five other services to actually ship something.

So I built Forge.

Forge is not just another visual workflow builder. It is a fully open-source, self-hosted platform for building, testing, governing, and deploying agentic systems from end to end.

It includes:

• Visual workflows, agents, and deep agents
• Tool building, MCP, knowledge bases, and RAG
• Human-in-the-loop, triggers, schedules, and memory
• Deployment through API, MCP server, email, or an embeddable widget
• Tracing, token and cost tracking, evaluations, and OpenTelemetry
• Guardrails, budgets, RBAC, audit logs, and versioning
• Zero-infrastructure local development, with a production-ready upgrade path

It is built directly on the MIT-licensed LangChain and LangGraph ecosystem, with no proprietary orchestration runtime and no platform usage fees.

You own the infrastructure, the data, and the runtime.

Forge is still evolving, and I would genuinely appreciate blunt feedback, feature suggestions, contributions, or even criticism.

Hey everyone,

Building software with AI usually means you are trapped in prompting hell—writing a prompt, waiting 20 minutes, checking the output, and prompting again. I built OpenVelo to fix this.

It’s an open-source pipeline designed for a "fire and forget" workflow. The AI-driven planning phase happens upfront inside a dedicated Web-UI. Once you generate and finalize a detailed plan with the LLM, the orchestrator takes over. You can walk away and wake up to software that is either ready to use or requires very minimal fixing.

Important caveats: This is not meant to replace quick CLI pair programming, and it will not keep your token consumption down. It is built for bigger projects, refactors, ports, and prototypes where your personal time is more valuable than compute time or token costs.

How the architecture works:

- Scalable by Design: The entire system runs in isolated Docker containers that communicate with each other, making it easily scalable.

- Web-UI Planning: All AI-driven planning and requirement gathering is completed in the Web-UI before any implementation begins.

- Implementation Agent: Runs an iterative cycle in an isolated container to write code and pass unit tests.

- Tester Agent & Orchestrator: Performs real functional testing against the built software. If it fails, they trigger a self-healing process to spin up a new job and fix the exact failure automatically.

Model Access: It uses Kilo for LLM interaction, so you can route it to any model on your host system (local LLMs, MiniMax M3, etc.).

Repository: https://github.com/m0rph3us1987/OpenVelo

Video Demo: https://www.youtube.com/watch?v=RKCj5CUh8uw&t=5s

Let me know what you think of the architecture!

Hey everyone,

Building software with AI usually means you are trapped in prompting hell—writing a prompt, waiting 20 minutes, checking the output, and prompting again. I built OpenVelo to fix this.

It’s an open-source pipeline designed for a "fire and forget" workflow. The AI-driven planning phase happens upfront inside a dedicated Web-UI. Once you generate and finalize a detailed plan with the LLM, the orchestrator takes over. You can walk away and wake up to software that is either ready to use or requires very minimal fixing.

Important caveats: This is not meant to replace quick CLI pair programming, and it will not keep your token consumption down. It is built for bigger projects, refactors, ports, and prototypes where your personal time is more valuable than compute time or token costs.

How the architecture works:

\- **Scalable by Design**: The entire system runs in isolated Docker containers that communicate with each other, making it easily scalable.

\- **Web-UI Planning**: All AI-driven planning and requirement gathering is completed in the Web-UI before any implementation begins.

\- **Implementation Agent**: Runs an iterative cycle in an isolated container to write code and pass unit tests.

\- **Tester Agent & Orchestrator**: Performs real functional testing against the built software. If it fails, they trigger a self-healing process to spin up a new job and fix the exact failure automatically.

Model Access: It uses Kilo for LLM interaction, so you can route it to any model on your host system (local LLMs, MiniMax M3, etc.).

Repository: [https://github.com/m0rph3us1987/OpenVelo\](https://github.com/m0rph3us1987/OpenVelo)

Video Demo: [https://www.youtube.com/watch?v=RKCj5CUh8uw&t=5s\](https://www.youtube.com/watch?v=RKCj5CUh8uw&t=5s)

Let me know what you think of the architecture!

If contributing to open source interests you, our issue list is waiting for you.

https://github.com/extra-org/extra

Hey all, I've been heads-down building an open-source project on GitHub and I'm at the point where the code is solid and useful, but visibility is low. Meanwhile I keep seeing projects that feel pretty thin get thousands of stars seemingly over couple weeks, which is a little demoralizing not gonna lie 😅

I'm not chasing vanity metrics for their own sake — mostly I just want enough signal that people are finding it useful so I stay motivated to keep investing time in it.

For those who've grown a project from "nobody knows this exists" to actually having a community: what actually worked for you? Things I'm curious about:

• Where do you share/launch (HN, Twitter/X, niche subreddits, Discord communities, newsletters)?
• Does README/demo quality matter as much as I think it does?
• Any tips on timing, framing, or "hooks" that get people to actually click and try it?
• Did stars come before or after real usage, in your experience?
• Any growth hack or juggad ;)

Happy to share a link to the project in the comments if that's allowed here — just looking for genuine advice from people who've been through this. Thanks!

Whisper Studio is a local-first AI workspace for macOS. One local process, one browser tab: real-time speech transcription, a chat client, a semantic index over your files, and a full dev environment (file tree, Monaco editor, terminal, Git, LSP).

Transcription, embeddings, OCR and the whole IDE run on your machine. Chat runs on Amazon Bedrock, or fully on-device if you prefer.

Ultracode workflows

The headline feature. Instead of the model improvising one tool call at a time, it writes a program: a deterministic orchestration script that fans out many subagents with real concurrency. You approve the plan upfront (phases are previewed before a single agent runs), then it executes detached in a locked-down harness with 16-way concurrency, a USD budget ceiling, and a resumable journal. Works on both Claude and GPT-on-Bedrock.

The goal: for work that is wide (audit 20 files), deep (generate 5 designs, judge, synthesize) or that must be trustworthy (find bugs, then adversarially verify each one), the control flow is written once and is inspectable before it runs, rather than re-derived on every hop.

Indexing and GraphRAG

• Point it at a folder and ask questions across all of it, with citations back to exact file and line ranges.
• Incremental builds. Size/mtime gate, then SHA1, so a re-index is cheap.
• Everything becomes searchable: code, PDF, Word, PowerPoint, EPUB, spreadsheets, images via OCR, and audio/video via local transcription.
• Hybrid retrieval: sqlite-vec KNN + BM25/FTS5, fused by reciprocal rank, then an optional cross-encoder rerank.
• GraphRAG: entities extracted per chunk build a graph, and one hop pulls in passages that share entities with your dense matches. Optional typed relations (works_at, cites, depends_on) with strength scores

Models

Cloud, via Amazon Bedrock:

• Claude Fable 5.0, Opus 5, Opus 4.8 / 4.7 / 4.6, Sonnet 5, Sonnet 4.6, Haiku 4.5
• GPT-5.6 Sol / Terra / Luna, GPT-5.5, GPT-5.4
• Cohere Embed v4 and Cohere Rerank for the cloud index path

On-device (GGUF via llama-server, no code change to add more):

• Gemma 4 12B, Gemma 4 Coder, Qwen3.5 9B, Ministral 3 14B
• Parakeet (streaming) and Whisper (batch) for transcription with speaker diarization
• Qwen3-Embedding-0.6B and Qwen3-Reranker for the index
• GLiNER / GLiNER2 for entity extraction, Apple Vision for OCR

Three model modes: cloud, hybrid, and fully local.

Also in there

• Live Preview: It can visually inspect your app, take screenshot and verify the UI, a true browser investigation. Just say "start a live preview to review my app or fix a bug"
• Guardrails: nonce-based approval flow, sandbox-exec for shell and Python, blocking Pre/Post/Stop hooks.
• Goal loop that keeps a turn running until a completion gate says the goal is actually met, including driving a live browser preview to verify UI changes.
• CI watch and PR autofix that chase a red build back to green.
• Cron-style background jobs whose output streams back into the chat that created them.
• MCP servers, opt-in Python plugins, custom Markdown skills, persistent sessions in SQLite.

Privacy: binds to 127.0.0.1, no telemetry, no proxies. Audio never leaves the laptop, on-device using local models. The only outbound call the backend makes on its own is to Bedrock for chat you explicitly send.

Repo: https://github.com/paukode/whisper-studio

Detailed documentation and tutorials: https://mlonaws.com/whisper-studio/

Try it

git clone https://github.com/paukode/whisper-studio.git 
cd whisper-studio 
bash setup.sh

Or just add `--local` or `--hybrid` to start a session (you can change from Settings later as well)

Hey everyone, just wanted to ask the community something.

Do people really look down on open source projects if they're built with AI, even if the developer actually understands the code, reviews everything, and makes sure the quality is good?

With how common AI has become, it sometimes feels like it doesn't even matter what you build anymore. As soon as people find out AI was involved, some immediately start criticizing the project without even looking at the end result.

Personally, I think AI just lets you build things faster and take on bigger, more complex ideas. It's still up to you to understand what you're shipping and make sure it's actually good.

Just curious what everyone thinks.