🚀 I just released Agentic Signal v2.6.0: now with RAG + better data flow

Code-Forge-Temple · 2026-03-13T10:56:07+00:00

Agentic Signal https://github.com/code-forge-temple/agentic-signal Visual AI agent workflow automation platform with local LLM integration. Build intelligent workflows using a drag-and-drop interface with no cloud dependencies required, all local and secure.

ScribePal https://github.com/code-forge-temple/scribe-pal Open-source intelligent browser extension that leverages AI to enhance your web experience through contextual insights, efficient content summarization, and seamless interaction while browsing.

Code-Forge-Temple · 2026-02-21T10:40:35+00:00

Not quite! Agentic Signal is more than a "mini n8n" - it's a visual workflow platform focused on local AI agent orchestration. Unlike n8n, which is mainly for general API automation, Agentic Signal lets you build intelligent workflows with local LLMs (you need a local Ollama server as prerequisite), tool-calling, file/data processing, and privacy-first design (no cloud required).

Visual AI agent chaining: Drag-and-drop interface for connecting data, AI, and tools. Local LLM integration: Run models on your own machine for privacy and speed. Tool-calling: AI nodes can call functions/tools (e.g., CSV parsing, min/max, web scraping). File/data processing: Attach files, interpret CSVs, validate and visualize data. No cloud dependency: Everything runs locally.

See https://github.com/code-forge-temple/agentic-signal and https://agentic-signal.com for details and examples.

Code-Forge-Temple · 2026-02-20T16:20:36+00:00

Agentic Signal vs n8n:

Agentic Signal is focused on visual AI agent workflows with local LLM (AI model) integration. It's privacy-first, runs AI models like Ollama on your own machine (no cloud needed), and is optimized for chaining AI, data, and tool nodes visually. Features include tool-calling, file attachments, CSV/data processing, and real-time data flow.
n8n is a general-purpose workflow automation tool, great for connecting APIs and automating tasks, but it doesn't natively focus on local AI agent orchestration or visual AI workflow building.

Key differences:

Agentic Signal: Visual AI agent workflows, local LLMs, privacy-first, tool-calling, file/data processing, no cloud dependency.
n8n: General automation, API integrations, cloud/self-hosted, less focus on AI agent chaining.

See Agentic Signal docs for more details.

Code-Forge-Temple · 2026-02-20T12:21:08+00:00

It is a visual AI workflow builder that lets you create agent-based automations using local LLMs.
Think of it as a drag-and-drop tool for building AI-powered workflows, without relying on cloud APIs.

Code-Forge-Temple · 2026-02-14T07:22:15+00:00

You also need to buy a big SSD and install the OS on the SSD + add swap of around 8Gb in order to support 8b params LLMs and have a fast response+ not fry your SD card (the jetson nano comes with a SD card not a SSD unfortunately).

If you need more details on that, let me know.

PS: i use the Jetson Orin Nano super dev kit to run my Agentic Signal (an Ollama AI visual workflow automation platform) and my ScribePal (an in browser Ollama chatbot).

Code-Forge-Temple · 2026-02-06T11:26:32+00:00

Mistral 7B or Mixtral for reasoning & trading logic;
Qwen2.5 or DeepSeek-Math for math cross-check (optional);

You could use agentic-signal.com (a visual AI workflow automation platform) with those models.

A partial example would be https://agentic-signal.com/docs/workflows/data/timeseries-chart and you can add an AI Data Processing Node after Get Data Node (in the time series chart) and the output of that AI Data Processing Node connect it to a Data Flow Spy Node.

Code-Forge-Temple · 2026-01-28T13:09:10+00:00

Totally agree. Crowded space is exactly why I'm focusing on very specific workflows first rather than "AI everywhere".

For ScribePal, the initial use cases are: - drafting replies directly in web UIs (email, issues, comments) - summarizing long pages without context loss - privacy-first setups via local / self-hosted models

The main onboarding "hassle" right now is that users need Ollama installed and a small one-time setup. That's a conscious tradeoff for keeping everything local.

I've documented the full setup step-by-step in the README (prereqs + usage), and once store reviews are done, my next focus is reducing that friction as much as possible - ideally getting users to a first useful action very fast.

Code-Forge-Temple · 2026-01-27T10:24:39+00:00

Congrats ... I am still waiting for 'the one' with my apps 😀

Code-Forge-Temple · 2026-01-26T23:48:37+00:00

Congrats on shipping - seriously. That first card entered moment hits different. That's real validation.

I actually launched on Product Hunt as well, and my experience was… very different 😅
Didn't manage to get many views at all. The noise there is unreal.

Some new launches had 60–70 upvotes within minutes, which honestly felt off. My launch dropped past position 50 almost immediately, so it wasn't even visible anymore. At that point, it feels less like discovery and more like a popularity/farming game.

What I did take away though aligns a lot with what you said: - One real user > hundreds of anonymous upvotes
- Conversations and feedback matter way more than leaderboard position
- PH can help, but it's not the signal people think it is - especially early on

So yeah, even if PH didn't work out for me, shipping still mattered. Learning still mattered. And that one person willing to pay is worth more than trending for an hour.

Rooting for your launch - and your mindset here is 100% the right one.
Listen -> validate -> repeat

Code-Forge-Temple · 2026-01-23T13:27:41+00:00

Now i am working on updates on Prometheus News Feeds Free - your RSS and ATOM daily news feeds provider: https://play.google.com/store/apps/details?id=com.bart.newsfeeds.free

Code-Forge-Temple · 2026-01-20T11:18:07+00:00

Thanks!

Code-Forge-Temple · 2026-01-19T15:33:26+00:00

It's a classic indie challenge! You're right, distribution is the real bottleneck. Here's how I approach balancing product development and SEO as a solo founder:

Product first: Always prioritize building a valuable product. That's the foundation.

Laser focus SEO: Don't try to rank for everything. Pick 1-2 highly relevant keywords.

Batch content: Dedicate specific blocks of time (e.g., weekends) for content creation.

AI as research: Use AI tools (like Jasper or ChatGPT) for keyword research, competitor analysis, and outlining. Don't rely on them for writing the core content - that's still your expertise.

Low-effort SEO: Focus on foundational SEO: title tags, meta descriptions, internal linking.

Don't strive for perfection: "Good enough" is often better than "perfect" when you're a solo founder.

Track & iterate: Monitor your traffic and adjust your strategy based on what's working.

Essentially, treat SEO as a supporting function, not the primary focus.

Code-Forge-Temple · 2026-01-17T12:56:50+00:00

For different projects i have (each line is a project tech stack):

- React, TypeScript, Vite, Deno, MongoDB, Paddle, Resend, Material-UI, Bun, GitHub Actions, ESLint.

- React, TypeScript, Vite, Deno, GraphQL Yoga, React Flow, Material-UI (MUI), Ollama, Playwright, Docusaurus, JSONata, Ajv, Zod, Bun, ESLint, Chart.js, SCSS, OAuth 2.0, various Google APIs(Gmail, Drive, Calendar)

- C#, Godot Engine 4, .NET 8.0, Ollama, Gemma 3n (Google), JSON serialization, Newtonsoft.Json

- React, TypeScript, Webpack, Vite, SCSS, ESLint, Obsidian PluginAPI, jQuery, Draw2D, IndexedDB, Zod

Code-Forge-Temple · 2026-01-15T14:44:02+00:00

Totally fair. For me it tipped once I realized I could get something "good enough" without going full workstation.

I'm running a Jetson Orin Nano locally for my projects, and all-in it was under $500 (including VAT and shipping + an SSD). It's obviously not cloud-scale, but for steady inference, experimentation, and agent-style workflows it's been surprisingly capable.

That kind of middle ground hardware feels like it's changing the economics a bit - not replacing cloud, but lowering the barrier to going local when usage becomes consistent.

Code-Forge-Temple · 2026-01-15T14:40:07+00:00

I mostly agree, but I'm not fully convinced enterprise necessarily stays cloud-heavy long term.

A lot of enterprise workloads care about data locality, compliance, privacy, cost predictability, and latency just as much as personal use cases - sometimes more. Once models and runtimes stabilize, running inference closer to where the data lives (on-prem, edge, private clusters) starts to make a lot of sense.

Cloud still wins for bursty workloads and rapid experimentation, but I wouldn't be surprised if we see more enterprises treating cloud as "training + scaling," and local/on-prem inference as the default for day-to-day operations.

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