Feed aggregator

We run an open startup and believe in sharing numbers publicly. But our setup was a mess: metrics lived in Stripe and PostHog, we'd pull them manually, paste into our CMS, and embed that on the site. Every update was a chore. Every number was stale. Openboard connects directly to your data sources and generates a live public metrics page. No CMS middleman, no manual updates. Currently supports Stripe (MRR, churn, active subscribers) and PostHog (visitors, signups, retention). Adding more integrations based on what people actually need. The base version is free — sharing should be frictionless. Paid plans unlock more integrations and custom domains. Would love feedback, especially from founders who already run an open page and know how annoying this problem is. openboard.fyi

Comments URL: https://news.ycombinator.com/item?id=47271798

Points: 1

# Comments: 0

Article URL: https://securityboulevard.com/2026/03/shinyhunters-claims-woflow-breach-what-it-means-for-saas-supply-chain-security/

Comments URL: https://news.ycombinator.com/item?id=47271790

Points: 1

# Comments: 0

I built EV Mapping to answer the question EV owners actually ask: "Can I get there and back without charging?"

You pick your car, drop a start pin, set your current charge %, and hit Compute Reach. You get two circles — outer for one-way range, inner for round-trip — plus all charging stations within reach as pins. Click any pin to open navigation in Google Maps.

Stack: vanilla JS + Leaflet, no build step, hosted on Vercel. Charger data merges OpenChargeMap and OpenStreetMap Overpass with deduplication. 700+ EV presets across 20+ markets (US, EU, India, China, AU, etc.) synced every 12h via a Python pipeline on GitHub Actions.

The catalog pipeline ingests fueleconomy.gov, afdc.energy.gov, cardekho.com, and greenvehicleguide.gov.au, plus region-native seed files. It has anti-regression guardrails and a canary→stable promotion workflow so bad syncs don't silently ship.

https://ev-mapping.vercel.app Source: https://github.com/novelmartis/ev-mapping

Feedback welcome — especially on range accuracy and charger pin coverage in non-US markets.

Comments URL: https://news.ycombinator.com/item?id=47271786

Points: 1

# Comments: 0

Article URL: https://www.bbc.com/news/articles/cddnn261n60o

Comments URL: https://news.ycombinator.com/item?id=47271783

Points: 2

# Comments: 0

Article URL: https://sitandr.github.io/typst-examples-book/book/

Comments URL: https://news.ycombinator.com/item?id=47271769

Points: 1

# Comments: 0

Article URL: https://electrek.co/2026/03/05/floating-wind-turbines-could-soon-power-ai-data-centers-at-sea/

Comments URL: https://news.ycombinator.com/item?id=47271509

Points: 1

# Comments: 0

Article URL: https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-gpt-5-4-in-microsoft-foundry/4499785

Comments URL: https://news.ycombinator.com/item?id=47271502

Points: 1

# Comments: 0

Article URL: https://yadin.com/notes/generated/

Comments URL: https://news.ycombinator.com/item?id=47271481

Points: 1

# Comments: 0

I’ve been building ABES (Adaptive Belief Ecology System), a memory architecture for AI agents based on the idea that memory should manage belief state over time, not only retrieve prior text.

The system models memory as structured beliefs with explicit state, including confidence, salience, contradiction pressure, lifecycle status, memory tier, evidence balance, lineage, user and session scope, and decay behavior. Beliefs can be reinforced, weakened, contested, updated, mutated, or deprecated as new evidence arrives.

The goal is to support longer-running agents that need to deal with stale information, conflicting information, confidence change, and belief revision, rather than only recalling similar prior content.

Current implementation includes a structured belief model, reinforcement and decay mechanics, contradiction handling, tiered memory behavior, session isolation, API support, Docker support, and testing/evaluation infrastructure.

What has been verified so far in the project’s published tests and evals:

822 passing tests

a 1,000-prompt evaluation with an overall score of 825/1000 (82.5%)

reported category scores of 96.8% episodic memory, 94.4% working memory, and 92.8% semantic memory

a 15-block side-by-side evaluation against a raw Ollama baseline, where ABES passed 14/15 blocks and the baseline passed 6/15

a 200-prompt cognitive stress test reported as 3 consecutive runs at 200/200

Two easy verification points:

run PYTHONPATH=$PWD pytest tests/ -q from the repo root

inspect results/side_by_side_eval.json for the block-level comparison output

I do not consider internal tests and project-published evals to be sufficient external validation. The next stages are stronger benchmarking, improved contradiction handling and belief revision, stronger temporal and relational structure, longer-horizon testing, multi-agent shared memory work, and better observability of belief transitions.

Comments URL: https://news.ycombinator.com/item?id=47271473

Points: 1

# Comments: 0

Article URL: https://workshop.humancompatible.ai/#call-for-posters

Comments URL: https://news.ycombinator.com/item?id=47271426

Points: 1

# Comments: 1

Article URL: https://www.ft.com/content/162076f9-3eed-4b11-9a36-d5717c8b357b

Comments URL: https://news.ycombinator.com/item?id=47271416

Points: 1

# Comments: 0

I built this as a side project because I found existing family tree tools either too bloated or too manual—I spent more time dragging boxes and aligning lines than actually mapping my family history.

My goal was to make it instant: you just add the names, and the auto-layout engine handles the hierarchy and spacing automatically. It runs entirely in the browser and exports high-res PNG/JPGs.

Would love feedback on the layout logic (especially for complex families) and the overall UI flow. Happy to answer any questions!

Comments URL: https://news.ycombinator.com/item?id=47271396

Points: 2

# Comments: 0

I have recently asked the same question in a HN thread, which was mysteriously downvoted. The question remains to me: there is a lot of talk between Anthropic and the DOW about adopting LLM technology for warfare. Specifically, for "fully autonomous weapons and mass domestic surveillance". Does anyone understand how these two goals can be achieved? LLMs don't seem to me the right tool for this. Autonomous weapons would require a much faster and much more reliable and deterministic AI. LLMs might be a better use for mass surveillance, but I am not really sure how they would cope with the massive amount of data and the limited context window (unless they use the data itself for training). RAGs might only mitigate the problem. Does anyone have some ideas?

Comments URL: https://news.ycombinator.com/item?id=47271391

Points: 1

# Comments: 2

Article URL: https://dorrit.pairsys.ai/

Comments URL: https://news.ycombinator.com/item?id=47271374

Points: 2

# Comments: 0

Hey HN, I've been talking to AI and health-tech founders lately, and something keeps coming up: patents feel like they were designed for a different world. By the time you spend $20k and wait 2–3 years, your startup has probably pivoted twice.

So I'm genuinely curious what people here are actually doing. Are you filing patents anyway? Keeping things as trade secrets? Publishing defensively? Or just not thinking about IP until there's funding on the table?

I threw together a really short survey (60 seconds, promise) to get some real data—no sales, just trying to understand what founders actually do: Form Link: https://forms.gle/8UAytkGNfge4GKrH8

If you'd rather just comment below, that's honestly just as helpful.

Comments URL: https://news.ycombinator.com/item?id=47271361

Points: 1

# Comments: 1

Built this after one too many production incidents caused by a renamed variable or missing key that CI never caught. EnvSentinel treats your .env as a versioned contract - you define the rules once in a JSON schema, then validate against it in CI, pre-commit, or locally. Also regenerates .env.example from the contract automatically so it never drifts. Pure Python stdlib, no external dependencies, 3.10+.

Comments URL: https://news.ycombinator.com/item?id=47271348

Points: 1

# Comments: 0

Article URL: https://www.economist.com/science-and-technology/2026/03/04/a-basket-of-new-fruit-varieties-is-coming-your-way

Comments URL: https://news.ycombinator.com/item?id=47271338

Points: 1

# Comments: 0

Article URL: https://link.aps.org/doi/10.1103/2dyh-yhrb

Comments URL: https://news.ycombinator.com/item?id=47271337

Points: 1

# Comments: 0

Article URL: https://qcut.app/

Comments URL: https://news.ycombinator.com/item?id=47271311

Points: 3

# Comments: 2

Article URL: https://github.com/swyxio/claude-compaction-viewer/

Comments URL: https://news.ycombinator.com/item?id=47271295

Points: 1

# Comments: 0