Show HN: Tab-league – archive Chrome tabs locally so closing them feels safe
Article URL: https://github.com/Shann5/tab-league Comments URL: https://news.ycombinator.com/item?id=50416248 Points: 1 # Comments: 0
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Show HN: Neural Network Formal Verification
Article URL: https://github.com/giovanni-norbedo/awesome-nn-formal-verification Comments URL: https://news.ycombinator.com/item?id=50412710 Points: 1 # Comments: 0
Show HN: Nino – Fastest C# serializer, safe on untrusted input, also TS/Go/Rust
Nino is a source-generated binary serializer for C#, and as far as we know the fastest one. In the same BenchmarkDotNet run on GitHub runners against MemoryPack 1.21.4, it serializes classes and polymorphic types 3.5–7.2x faster and deserializes them 1.7–2.1x faster (arrays of unmanaged structs are on par). Deserializing into existing objects allocates nothing. Reproduce: https://nino.xgamedev.net/en/docs/v4/benchmarksIt is built to read bytes you do not trust: lengths are checked before allocating, depth is bounded, hash flooding is limited, and the read paths are fuzzed in CI.The bytes are a spec, not a memory dump, so CoreCLR, Mono, IL2CPP and 32-bit hosts write the same bytes, and runtimes for TypeScript, Go and Rust pass the same conformance corpus.Trade-offs: positional by default (VersionTolerant or Keyed for stored data), a 4-byte type id on polymorphic values, no System.Type, needs C# 9 and Roslyn 4.3+.Docs: https://nino.xgamedev.net Comments URL: https://news.ycombinator.com/item?id=50413601 Points: 1 # Comments: 0
Show HN: Shutdown Watch – App shutdowns, export deadlines, and alternatives
My recipe app picked up users when a similar app shut down, and I kept noticing people scrambling to export their data, so I created this project to scratch my own itch.The 3 main purposes this serves are:1. Give the public a way to find how to export their data or find a replacement if one of the apps they use shuts down.2. Give app creators a way to get their apps in front of people who are actively looking for a replacement for a similar app. Sponsored spots are labeled and never change the dates or export steps.3. Provide inspiration for app creators looking for ideas for what to build next. Just because apps shut down doesn't mean they were not good ideas; many times they shut down for other reasons, like poor execution, no marketing, over-spending, etc.Every date and export step links to the company's own notice.I'm always open to ideas to make it more useful. I read all the comments and constructive criticism and input are always welcomed. Comments URL: https://news.ycombinator.com/item?id=50412639 Points: 1 # Comments: 0
Show HN: We Build Shareable Context Layer for Agent Teams
Article URL: https://github.com/volcengine/OpenViking Comments URL: https://news.ycombinator.com/item?id=50410477 Points: 1 # Comments: 1
Show HN: Umbel – Remote-control agent CLIs over tmux
Been using this in all my sessions the last months. Opus 5.5 (over MCP) spawns Deepseek flash agents - and codex for audits etc. Very curious to hear what the HN crowd thinks/makes of it. Comments URL: https://news.ycombinator.com/item?id=50383189 Points: 1 # Comments: 0
Towards Robust Long-Lived Agents: Benchmarking Experience Lifecycles in Evolving Environments
arXiv:2611.05450v1 Announce Type: new Abstract: As agents become more capable, they are moving beyond one-off tasks toward sustained assistance across successive user goals. Such assistance requires agents to discover how unfamiliar environments work, reuse what they learn across tasks, and keep that experience current as environments evolve. Existing benchmarks increasingly study experience reuse and adaptation, but provide limited insight into which parts of accumulated experience agents revise, retain, or lose. To address this gap, we introduce ReScopeBench, a benchmark for the experience lifecycle across 18 interactive environments. It distinguishes structural experience about how an environment is organized and accessed from rule experience about reusable principles and constraints that guide decisions across tasks. To study how experience is acquired, reused, and revised, we propose an experience-first construction approach. It builds task streams around shared experience and introduces silent environmental changes. We evaluate task performance and track changes in saved experience throughout each stream. Across 20 models, we find that retained experience improves task performance, but agents struggle to keep it current as environments change. Even when the required experience is correctly recorded, agents do not always complete tasks successfully. These findings identify selective maintenance and effective use as complementary challenges for long-lived agents.
General Compute raises $100 million Series A, countering the neocloud disaster narrative
General Compute is betting on inference chips from outside the Nvidia ecosystem.
Merge-Based Vocabulary Extension Stalls at the Pre-Tokenizer: An English-Preserving Repair for Nepali, Tested in Continued Pretraining
arXiv:2610.33016v1 Announce Type: new Abstract: Llama-3, Qwen3 and many other open LLMs pre-tokenize text with a regular expression whose word class admits only Unicode letters. Devanagari vowel signs are combining marks, so this regex splits Nepali words at every vowel sign, and models that use it spend 2.6 to 5.8 times as many tokens on Nepali as on the same English text. Merge-based vocabulary extension cannot join the split pieces: for Llama-3 and Qwen3 no number of new merges brings this premium below about 2.2, and ten released extensions already end within 4% of this pre-token floor. We give a small edit to the deployed regex that provably keeps every token id of text without combining marks or Devanagari characters. With 32,000 new merges it brings Nepali to 1.01 times English, abo
Phronix: The Capability-Centric Learning Paradigm for Agents
arXiv:2610.31789v1 Announce Type: new Abstract: Scientific computing increasingly relies on large language model-based agents for performance engineering tasks such as instrumentation, analysis, and workflow porting. However, agent effectiveness depends heavily on the surrounding harness, which typically relies on manually written skills and curated memory. This limits autonomous learning, forcing agents to repeatedly rediscover solutions rather than retain and reuse knowledge from prior executions. We introduce Phronix, a capability-centric learning paradigm that enables agentic harnesses to acquire, validate, and refine reusable capabilities from their own executions. Phronix operates through a predictor-validator-corrector loop: MCP tools and associated skills propose solutions, tool ex
Learning What to Ask for Tomorrow: Active Preference Acquisition for Long-Term Personalized Web Agents
arXiv:2610.31107v1 Announce Type: new Abstract: Web agents increasingly serve the same user across repeated online tasks such as booking travel, shopping, and scheduling, so their long-term usefulness depends on how well they know the user's preferences. Passive memory and personalization methods cannot obtain preferences the user never reveals, and clarification methods that value a question by its benefit to the current task overlook the benefit of reusing the answer in later tasks. We study Active Preference Acquisition (APA): which missing preferences are worth the cost of asking? We define the Prospective Personalization Value (PPV) of a missing preference as its expected cumulative utility gain over future tasks minus the effort and disclosure-risk cost of asking, and select question