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Article URL: https://ratfactor.com/cards/fitted-sheets-pro-unfold

Comments URL: https://news.ycombinator.com/item?id=47240008

Points: 1

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Article URL: https://docs.getbifrost.ai/quickstart/gateway/setting-up-auth

Comments URL: https://news.ycombinator.com/item?id=47240003

Points: 1

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Article URL: https://github.com/aayoawoyemi/Ori-Mnemos

Comments URL: https://news.ycombinator.com/item?id=47239986

Points: 1

# Comments: 1

Most ML frameworks optimize for leaderboard accuracy. But in finance and healthcare, accuracy is often the least interesting part of the system. If you can’t explain a prediction, you can’t deploy it. If your probabilities aren’t calibrated, you can’t trust them. If your pipeline doesn’t enforce constraints, you can’t ship it. I built Endgame after repeatedly running into that gap in production.

Anti-money laundering (banks) Early in my career, I was hired to improve an anti-money laundering system. The incumbent model was 28 hard-coded rules. If enough thresholds fired (e.g., $3,000 ATM withdrawals over 30 days), the account was flagged. No one knew where the thresholds came from. There was no modeling of the underlying behavior. Just rule accumulation. I convinced the bank to provide the raw financial features behind those rule firings. We trained an interpretable ML model directly on the underlying activity patterns. The result: ~200% more true positives (accounts actually involved in fraud or laundering). But what leadership cared about most wasn’t the metric. It was this: “Why is this account suspicious?” That theme repeated across industries.

Insurance claim adjudication I later built a claim adjudication model for a major health insurer. The legacy system was massive, brittle, and effectively a black box. It would frequently deny claims incorrectly, and no one fully understood how it worked. We built a new ML system that brought claim-level adjudication accuracy to ~95%. Again, the metric wasn’t the headline internally. The headline was: “Why did this claim get denied?” In regulated environments, interpretability isn’t optional.

Stock forecasting and calibration I also learned this lesson personally. I built stock-forecasting models that performed well in historical backtests. Some predictions showed 80% probability of a price increase. Then the market regime shifted. The probabilities were overconfident. Some trades went the opposite direction. I lost money. Accuracy ≠ trustworthy probabilities. Calibration and drift awareness matter far more in deployment than most tutorials suggest. That experience fundamentally changed how I think about ML systems.

The core idea Endgame is my attempt to encode those lessons into a framework. It’s not trying to replace scikit-learn. Every estimator implements fit / predict / transform. But it extends the ecosystem with: Glass-box models (EBM, GAM, CORELS, SLIM, GOSDT, etc.) SOTA deep tabular models (FT-Transformer, TabPFN, SAINT, etc.) Conformal prediction and Venn-ABERS calibration Deployment guardrails (leakage detection, latency constraints, drift checks) 42 self-contained HTML visualizations Super Learner, BMA, cascade ensembles A full AutoML pipeline that respects deployment constraints All under a unified sklearn-compatible API.

Agent-native ML (MCP) We’re in the agentic AI era. You can ask an LLM to build a pipeline for you, but it often requires multiple prompts and manual corrections. Endgame ships with a native MCP server. This lets agents: load data train models compare results generate reports export reproducible scripts Through structured tool calls, not fragile prompt chains. My belief is that ML pipelines will increasingly become conversational infrastructure.

A small contrarian view The ML community is underestimating the problems left to solve in tabular data and overestimating the demand for accuracy-optimized models. Most real-world data in business, healthcare, and finance is tabular (often multimodal). And most real-world systems need to be interpretable, calibrated, and deployable — not just accurate.

Endgame v1.0.0 is open source (Apache 2.0). Python 3.10+. If you work on production ML systems, especially in regulated domains, I’d genuinely value feedback. GitHub: https://github.com/allianceai/endgame Install: pip install endgame-ml Happy to answer technical questions.

Comments URL: https://news.ycombinator.com/item?id=47239982

Points: 1

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I built onWatch because I was checking 6 different AI provider dashboards every day to track my quota usage. Each has different billing cycles, different formats, and none show historical patterns.

onWatch is a Go CLI that runs as a background daemon, polls your API quotas (Anthropic, OpenAI Codex, GitHub Copilot, Synthetic, Z.ai, Antigravity), stores history in SQLite, and serves a Material Design 3 web dashboard.

Key decisions:

- Single binary (~13MB), no runtime dependencies - <50MB RAM with all 6 providers polling in parallel - All data stays local — zero telemetry, no cloud - One-line install for macOS, Linux, Windows - Docker support (distroless, non-root, ~10MB image)

The insight I kept coming back to: knowing your current usage isn't enough. You need historical cycle data — which sessions burn quota fastest, how usage compares across billing periods, and when to expect resets.

Install: `curl -fsSL https://raw.githubusercontent.com/onllm-dev/onwatch/main/install.sh | bash`

https://github.com/onllm-dev/onwatch

Comments URL: https://news.ycombinator.com/item?id=47239970

Points: 1

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Article URL: https://strlen.com/lobster/

Comments URL: https://news.ycombinator.com/item?id=47239955

Points: 1

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Article URL: https://www.scientificamerican.com/article/why-developers-using-ai-are-working-longer-hours/

Comments URL: https://news.ycombinator.com/item?id=47239404

Points: 1

# Comments: 1

My wife is a product manager and she kept asking me questions about APIs at her new job. I’m a former developer and now a high school teacher, and I love simplifying complex tech concepts.

So we created API101.org, a free course to help product managers and non-technical folks understand APIs, webhooks, authentication, and more. Most of the course is reading and examples, but there are a few mini Postman-style widgets (her idea) scattered in the lessons so you can try real API calls right in your browser.

It’s about 2–3 hours long, fairly comprehensive (maybe a bit much, but everything felt essential), and built as a side project for fun.

We’d love feedback, what’s useful, what’s confusing, what could be improved. Thanks!

Comments URL: https://news.ycombinator.com/item?id=47239396

Points: 1

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Article URL: https://blog.overshoot.ai/blog/qwen3.5-on-overshoot

Comments URL: https://news.ycombinator.com/item?id=47239393

Points: 1

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Article URL: https://nimasadri11.github.io/random/voice-input-agents.html

Comments URL: https://news.ycombinator.com/item?id=47239366

Points: 1

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Article URL: https://humanprogress.org/a-vindication-of-bjorn-lomborg/

Comments URL: https://news.ycombinator.com/item?id=47239351

Points: 1

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Article URL: https://wjamesau.substack.com/p/warning-llms-able-to-de-anonymize

Comments URL: https://news.ycombinator.com/item?id=47239350

Points: 1

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Article URL: https://www.joanwestenberg.com/a-soft-landing-manual-for-the-second-gilded-age/

Comments URL: https://news.ycombinator.com/item?id=47239323

Points: 1

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Article URL: https://github.com/CharlesWiltgen/Axiom

Comments URL: https://news.ycombinator.com/item?id=47239283

Points: 1

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Article URL: https://www.pewresearch.org/internet/2026/02/24/how-teens-use-and-view-ai/

Comments URL: https://news.ycombinator.com/item?id=47239275

Points: 1

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Article URL: https://www.science.org/content/article/meet-three-scientists-who-said-no-epstein

Comments URL: https://news.ycombinator.com/item?id=47239264

Points: 4

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Article URL: https://www.trustloop.live/

Comments URL: https://news.ycombinator.com/item?id=47239224

Points: 1

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Article URL: https://services.google.com/fh/files/misc/cybersecurity-forecast-2026-en.pdf

Comments URL: https://news.ycombinator.com/item?id=47239174

Points: 1

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Article URL: https://wordnet-vis.onrender.com/

Comments URL: https://news.ycombinator.com/item?id=47239148

Points: 1

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Article URL: https://daydreamsinruby.com/blog/2026-02-23-aligned-offsite-outcomes/

Comments URL: https://news.ycombinator.com/item?id=47239138

Points: 1

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