Ask HN: What Are You Working On? (April 2026)

A MVP is live at this https://ai-eval-lab.janardan.xyz/

Github: https://github.com/janardannn/ai-eval-lab

For now, for every session, inside the docker container, i create a virtual frame buffer using xvfb and render KiCad in it. Thereafter using x11vnc to pickup the xvfb display and serve it using VNC. Since browsers cant use raw TCP connections, i am using websockify to bridge WS TCP. To render the app in browser, I am using noVNC to render an iframe inside my app. noVNC opens a websocket to websockify and the frames from the container start flowing.

Inside, KiCad itself, a have added a python plugin which auto loads when KiCad is started as a background thread. After a set interval of time, it tracks all the board objects like footprints, tracks, zones etc and serialize them as a JSON and makes a POST request to the backend server for the specific session id.

For the grading and evaluation, as of now, I am using an LLM to cross check the entire sequential board activity that was sent over the duration of the assessment and grade it against a predefined rubric/score it against per-checkpoint criteria. This is entirely based on how the admin have set the assessment grading rubrics.

I am not building it as a business rather exploring the space and the technology. It was an excuse for me to learn about VNC, streaming, docker orchestration and also somewhat of AWS through its deployment.

I'm hosting it on the free AWS tier so only one session at a time. If you queued then please give it some time.

Happy to chat about anything regarding the project!

Current features: Using zero-knowledge proofs for a proof of personhood (no personal data shared), Reputation as a conviction signal and gamification (gain reputation), Blind voting phase to fight herding, Using a public ledger/blockchain in the background for transparency

We know that the current implementation is far from a perfect rating system, but that’s why we are here, trying to get as much feedback as possible and finding the right balance between hard to attack and user-friendly.

The gap I kept seeing: small SaaS teams (5-50 people) are actively competing but either have no CI process, or one person manually checking competitor websites. They want structured tracking but the enterprise tools are priced for enterprise budgets.

Current pivot: also building a white-label agency portal — agencies that deliver competitive intelligence to clients are currently doing it via Google Docs or Notion exports. Building a branded client portal so they can deliver CI professionally.

Stack: Cloudflare Workers + D1 + R2, Vite/React 19. Waitlist open at peerscope-waitlist.pages.dev — trying to validate demand before building too much.

Biggest challenge: getting in front of agency owners who actually do CI work for clients. If that's you, happy to chat.

ironically the difficult part wasnt the OCR or the textanalyses, but finding the exact position of the text in the input image.