Mljar Studio
Hi HN, I’ve been working on mljar-supervised (open-source AutoML for tabular data) for a few years. Recently I built a desktop app around it called MLJAR Studio. The idea is simple: you talk to your data in natural language, the AI generates Python code, executes it locally, and the whole conversation becomes a reproducible notebook (*.ipynb file). So instead of just chatting with data, you end up with something you can inspect, modify, and rerun. What MLJAR Studio does: - Sets up a local Python environment automatically, runs on Mac, Windows, and Linux - Installs missing packages during the conversation - Built-in AutoML for tabular data (classification, regression, multiclass) - Works with standard Python libraries (pandas, matplotlib, etc.) - Works with any data file: CSV, Excel, Stata, Parquet ... - Connects to PostgreSQL, MySQL, SQL Server, Snowflake, Databricks, and Supabase. For AI: use Ollama locally (zero data egress), bring your own OpenAI key, or use MLJAR AI add-on. I built this because I wanted something between Jupyter Notebook (flexible but manual) and AI tools that generate code but don’t preserve the workflow. Most tools I tried either hide too much or don’t give reproducible results and are cloud based Demos: - 60-second demo: https://youtu.be/BjxpZYRiY4c - Full 3-minute analysis: https://youtu.be/1DHMMxaNJxI Pricing is $199 one-time, with a 7-day trial. Curious if this is useful for others doing real data work, or if I’m solving my own problem here. Happy to answer questions.
AI Analysis
Analysis coming soon.
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We post-trained a model that pen tests instead of refusing your code
I'm Dimitrios at Cosine. Quick orientation first: the read-only scan is free and you can run it right now: that's the part to try. The pen-test mode is gated behind written authorisation, because it's live offensive testing against real systems; I'll explain that below, it's not a paywall thing. The reason this exists: most "AI security" tools wrap a general model, so they inherit its refusals, point one at a real offensive task and it hedges or declines, because the base model was trained to. We went the other way and post-trained our own model for offensive security, so it does the work instead of apologising for it. It's our model, not a wrapper. Under the hood it's a multi-agent swarm: an orchestrator splits the job across subagents running in parallel, each owning a slice, then synthesises one report. That's what gets a polyglot microservice repo done in one pass. The fair objection to a model that doesn't refuse, pointed at your code: how is that not reckless? I think refusals are the wrong layer to put safety in. A model that refuses is both useless (won't do the job) and unsafe (you're trusting a probability distribution to hold a hard line). So we don't ask the model to behave — we enforce it in the harness. A runtime guard written in Go intercepts every tool call before it runs. In scan mode it hard-blocks every mutating tool and any non-read-only shell command and the model can decide whatever it wants, the guard won't let it write. In pen-test mode the same guard pins the agent's network scope to the targets you authorised; it can't reach anything else. Safety is deterministic and sits below the model, not inside it. Two modes, one CLI: - Security Scan - read-only audit of a local codebase, every finding tied to a file and line. Free, runnable today. - Pen Test - the swarm attacks systems you authorise and hands back the request it sent and the response your code gave. Gated behind written authorisation. Demo target and to be straight about it: Bank of Anthos, Google's open-source reference bank. Known app, some intentionally-soft bits — which is why I picked it, so you can reproduce the run instead of trusting a screenshot. The scan found an integer overflow in the transfer path that would let you forge an account balance, plus the usual injection/auth/secrets classes. It's a closed binary (brew/curl/winget), runs locally, by Cosine. Run it behind a firewall and `tcpdump` exactly what it does before you trust it on anything real. Install is free; the scan runs on a $20 Cosine subscription; pen test is scoped per engagement. I'll be in the thread all day. The harness-vs-refusals design is the part I most want torn apart - tell me where it breaks.
ABC Classic 100 Rankings visualised
This weekend is the ABC Classic FM countdown, which prompted me to dust off an old un-published data visualisation of rankings from previous years. I've considered adding a search function, but I also kind of like that it requires a bit of exploration in the current form. Some of the code is a bit clunky and I wouldn't mind refactoring it. I'm also not sure about browser compatibility - I've only got access to a couple of devices to test it on.