7 posts tagged with "open-source"

The message arrived on a Thursday evening. Forwarded twice already. The text looked official: a government energy rebate notification, a pending payment of £400, a link to "verify your bank details before the payment window closes Friday." The sender was her son's colleague. Nobody had checked where it came from first.

She forwarded it to her son before clicking. He was about to say it looked fine. Then he noticed the URL: gov-rebate-energy.co.uk. Not gov.uk. Not energy.gov.uk. A registered domain, clean design, HTTPS padlock, professional enough to pass a quick read. He told her not to click. She'd been one browser tab away from entering her sort code and account number into a credential-harvesting form.

This near-miss required one thing: a second person who knew what to look for. Most people don't have that. That's what I'm trying to build.

She sent 47 applications in June. Got three responses — two automated rejections and one screening call for a "graduate entry" role that turned out to require three years of commercial Python experience. She kept a spreadsheet. Most rows stayed blank.

This isn't a personal story of bad luck. It's what the ONS is measuring when it reports that employment concern among UK adults rose from 31% in June 2024 to 54% in June 2026 — the highest level since the survey began. The headline is anxiety; the substrate is a market where 14% of entry-level postings disappeared year-on-year, ATS systems silently discard CVs against narrow keyword thresholds, and job aggregators cheerfully surface the same role six times from six different scrapers.

The current generation of AI job tools makes this worse. They optimise CVs to game ATS keyword matching. They automate bulk applications, violating employer terms of service. They give candidates the dopamine hit of "sent" with no honest signal of fit.

I built the UK Graduate Career Navigator this week as a direct counter to that. It has Claude on the inside. It is not trying to do more than a candidate should trust an AI to do.

Ten weeks, nine posts, and four working systems.

The practical question behind all of it: how do you build useful AI systems where cost, connectivity, data privacy, and human accountability are hard constraints?

The industry narrative is dominated by cloud-first, fully autonomous agents operating with unlimited budgets. JigsawFlux took a different direction: local inference, offline-first architectures, Kubernetes home labs, and strict state-machine controls.

The path is visible in retrospect: direct local LLM wrappers gave way to stateful agent workflows, which gave way to bounded edge systems for clinical consultation, travel risk assessment, and NHS appointment recovery.

This is what those nine posts covered, what connects them, and the five engineering rules that emerged from the work.

The first decision in any agentic project isn't which model to use. It's which framework will orchestrate it. Get that wrong and you inherit a stack you can't run locally, can't afford to scale, and can't escape when the vendor changes the API.

This is a JigsawFlux project. JigsawFlux builds open-source tools for health tech, humanitarian response, and crisis management — in places where "cloud-native" is not an option and IT budgets are measured in grants, not headcount. That context imposes hard constraints on every architecture decision: portability, cost, and freedom from vendor lock-in.

The frameworks here — LangGraph, CrewAI, and AutoGen — were chosen because they meet those constraints. They are open source, actively maintained, and run entirely on hardware you own. Alternatives like Microsoft Semantic Kernel or Amazon Bedrock Agents are capable, but they introduce hard dependencies on specific cloud ecosystems. That trade-off doesn't fit the JigsawFlux model.

In Part 1 I ran Ollama directly on a Linux machine and wired it up through an MCP layer to a small web app. It worked. But bare-metal has friction — if the process crashes, it stays down. Adding Open-WebUI means managing another process. Resource limits are manual. There's no clean internal networking between services.

This post moves the whole thing into Kubernetes. The goal isn't enterprise-grade infrastructure — it's a home lab setup that's reliable, easy to extend, and honest about its limitations.

Manifests are in the ollama-mcp-starter repo under backend/k8s-deployment/.

Welcome to the JigsawFlux Blog — a space for updates, ideas, and stories from the JigsawFlux open-source community.

AI inference doesn't have to mean a cloud API call. This post walks through a spike I built to run a locally hosted language model through a clean, layered architecture using Ollama and the Model Context Protocol (MCP).

The full source is on GitHub. Contributions and feedback welcome.