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Psychological Warfare in Reverse Engineering (2015)

Ancient Library – 1,060 Greek/Latin texts, click any word to parse it

Databricks drove down AI coding spend 70%

Oracle bans AI-generated code from OpenJDK

Iceberg Collapses and Flips over in Ilulissat, Greenland (July 25, 2026) [video]

Software is about people, not code

Show HN: Lefts – a domain specific language for building creative ML models Lefts is a small domain specific language for applied machine learning modelling. It is aimed at anyone that builds predictive models for a living and wants to focus on reasoning about model behaviour and building creative architectures, and not on building train&#x2F;test pipelines or worrying about data leakage.<p>It is simple but quite powerful - I have been using it in my own work to explore new ways of modelling (check out the tutorial on geometric models!), and to breeze past the least interesting parts of being a machine learning engineer. It also has some cool functional programming going on under the hood (check out the design philosophy!).

Show HN: Wyzer Programming Language So i&#x27;ve been working on this project since a few days (or months i should say), it&#x27;s called wyzer (meaning wiser) it&#x27;s a statically typed, compiled, resource-oriented programming language with integrated distributed safety via choreographic programming and perceus memory model, The reason why i began this project is out of frustration from Rust, you see it does provide safety for your memory by the strict type checking but what it does not gurantee safety against are distributed deadlocks which is basically a few independent nodes or services wait permanently for resources or messages held by each other, forming a circular wait, the rest are cross-service correctness and protocol mismatch as well. If we are specific over here Wyzer works on mainly generalizing the concept of choreographic programming in a high level programming language because its the very few attempts of actually solving these gaps of safety. Instead of borrow checkers and lifetimes wyzer has linear&#x2F;affine types and a perceus reference counting which is computationally much simpler for an LSP to understand as well<p>after 5 months of research and a few weeks of development i am soon going to release version 0.1.0 of it, if you would like to contribute to it you&#x27;re most welcome!

Graph-Native Infrastructure for Context and Accountable AI Systems

A self-improving RLM agent for coding workflows and long-running autonomous tasks.

The Gargantuan Lie That Is Collapsing the Climate

Show HN: textlog – A quiet, text-only microblogging platform, open-source, no JS

Kalshi and Polymarket bets on clinical trials criticized as 'ghastly'

Show HN: Recipe Jar, a local-first recipe keeper with no account or ads

Show HN: File-based HTTP endpoints for Python with its own isolated dependencies

Show HN: Certo – An open source platform to deliver Open Badges Certo is open-source infrastructure for issuing, managing, verifying, and exchanging digital credentials.<p>It implements Open Badges 3.0[1] and W3C Verifiable Credentials[2] which are the open standards that make credentials portable, machine-verifiable, and vendor-independent.<p>It is designed to be:<p>- Self-hosted: run it inside your own infrastructure, air-gapped network, or sovereign cloud - Standards-compliant: credentials it issues work with any OB3&#x2F;VC-compatible system - API-first: everything the UI can do, the API can do - Exportable: your data is always yours, in open formats - Extensible: a small, stable core with a plugin interface for everything else<p>A demo is available at <a href="https:&#x2F;&#x2F;certo.schroedinger-hat.org&#x2F;" rel="nofollow">https:&#x2F;&#x2F;certo.schroedinger-hat.org&#x2F;</a> which we&#x27;re using for Schrödinger Hat[3] conferences, workshops, partner communities.<p>I&#x27;ll be around if you have any questions. Feel free to give a feedback on the project.<p>[1] <a href="https:&#x2F;&#x2F;www.imsglobal.org&#x2F;spec&#x2F;ob&#x2F;v3p0" rel="nofollow">https:&#x2F;&#x2F;www.imsglobal.org&#x2F;spec&#x2F;ob&#x2F;v3p0</a> [2] <a href="https:&#x2F;&#x2F;www.w3.org&#x2F;TR&#x2F;vc-data-model&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.w3.org&#x2F;TR&#x2F;vc-data-model&#x2F;</a> [3] <a href="https:&#x2F;&#x2F;schroedinger-hat.org&#x2F;" rel="nofollow">https:&#x2F;&#x2F;schroedinger-hat.org&#x2F;</a>

2027 memory capacity is reportedly sold out

Show HN: Remembrane – agent memory in one SQLite file, zero dependencies This is a small library for giving an agent persistent memory without running any infrastructure. The whole store is one SQLite file, and the default install has no dependencies. I built it because whenever I wanted an agent to remember a handful of facts across sessions, the options were a hosted API, a vector database, or a framework, and that felt like too much for what is usually a few thousand short strings.<p>The part I find most useful is that recall is deterministic, so you can write unit tests that assert what your agent remembers and run them in CI. I haven&#x27;t seen that elsewhere and it&#x27;s what I rely on most. Beyond that: it&#x27;s one file you can copy, inspect, or delete, with no server or background process; every result can show its own score breakdown, so ranking isn&#x27;t a black box; and every change is journaled, so you can snapshot the store and diff it later. It also exposes an MCP server, so an MCP-capable agent like Claude can use it directly, and there are LangChain and CrewAI adapters.<p>It ranks by similarity plus recency, importance, and whether a memory has been useful before, and those weights are configurable, including turning recency off. There&#x27;s also a check that flags when two memories contradict each other, but it&#x27;s a heuristic, and I&#x27;d treat its output as candidates to review rather than ground truth.<p>Limits, up front: the default embedder is lexical, not semantic, so for real semantic recall you plug in sentence-transformers or OpenAI with one line. It&#x27;s meant for agent-scale memory, thousands of items rather than millions; past roughly 50k you&#x27;ve outgrown the design and should use a vector database. None of the recency or conflict ideas are novel either. Systems like Zep have done temporal memory for a while; my only claim is that you can get a useful version of it in a dependency-free file you can test.<p>I did compare it to mem0, and I want to be clear about what that does and doesn&#x27;t show. I ran mem0 in its no-LLM mode (infer=False) with the same embedder, so it only measures the storage and ranking layer, not mem0&#x27;s LLM extraction, which is its main value. In that narrow setting remembrane was faster, used less storage, and returned updated facts more often because it accounts for recency. That&#x27;s a substrate comparison, not a claim to be better at memory overall. The numbers, and the cases where my default embedder loses, are in BENCHMARKS.md, and it reproduces in a couple of installs.<p>I&#x27;ve written up the known gaps as issues: the CrewAI adapter is a helper rather than a drop-in backend so far, the benchmark should be extended to a public retrieval dataset with no LLM calls, and recall could use diversity-aware re-ranking so it doesn&#x27;t return near-duplicates. Contributions welcome.<p>On disclosure: I wrote this with Claude, made the design decisions myself, and I maintain it. I also had a second coding agent try to break each release, which surfaced some real bugs, including a cache-coherence issue under concurrent writers and a counterexample to a packing-optimality claim I had made. Those are fixed and are now regression tests. I&#x27;d rather you judge the tests and the changelog than take my word for it.<p>Happy to hear where it falls short.

Show HN: XSAF – Extra Small Agent Framework

Artificial Intelligence used to design new viruses