#开源/仓库

I optimized SQLite 3. The optimized build achieves a verified geometric-mean speedup of 1.59x over the pristine latest trunk across four benchmarks: - 2.06x on the official speedtest1 (~30k statements) - 1.90x on TATP (OLTP mix: 400k transactions, 100k subscribers) - 1.30x on the Star Schema Benchmark (13 queries x 2, 1.5M-row lineorder) - 1.25x on kvtest (blob I/O: 40k x 10 KB, seq + random + update) All 1,032,940 cases in the full SQLite test suite pass, and every benchmark run produces checksum-verified identical results. For context: the SQLite team has spent nearly 20 years tuning this code; their own measurements show ~3.5x total CPU improvement since 2008, earned a few percent at a time. Tested so far on standard Linux provided by GCP. An agent called KISS Sorcar did it in under 8 hours for under $150 in API cost. I wrote no code: 1 main short prompt plus a couple of steering prompts. Why I trust the results: 1. No cheating in the speedup. The biggest win is defaulting to WAL journaling, the configuration SQLite's own docs recommend. Re-measured durability-neutral (synchronous=FULL: committed transactions survive power loss exactly as strongly as the baseline), it is still 1.54x faster. 2. Adversarial testing. A separate attacking agent tried to break the changes: a 37-script differential SQL corpus (recursive CTEs, window functions, triggers, UPSERT, JSON, FTS5, rtree, corrupt inputs) compared byte-for-byte against a pristine build under ASan/UBSan, plus WAL-file corruption, multi-process mptest, fd-exhaustion, symlink, and read-only-media attacks. 3. Security hardening and independent review. Two hardening rounds with kimi-k3 as the model: in-tree fuzzers (fuzzcheck over all 8 corpora, sessionfuzz) came back clean; 14 hostile-WAL corruption scenarios - no crashes; OOM injection and page-size sweeps; one real bug found and fixed. 1.59x is what an agent can honestly find in one of the most heavily optimized codebases in the world overnight. Optimized SQLite Repository: https://github.com/ksenxx/sqlite-optimized/ Blog: https://kisssorcar.github.io/blog/sqlite-optimization-blog.html

Introducing EdgeBench, a benchmark designed to study how agents learn from environments over at least 12~72-hour runs. We find that performance follows a log-sigmoid function of environment interaction time with high precision. EdgeBench is built with three ingredients: - 🌍 Real & Diverse: 134 real-world tasks across 6 task categories, spanning scientific problems, professional knowledge work, software engineering, optimization, formal math, and games. - ⏳ Ultra-Long-Horizon: Each task supports 12–72 hours of agent work. Recorded human effort averages 57.2 hours. - 🔁 Informative Feedback: Agents receive real-world feedback for continuous improvement. After 38,000 hours of agent runs on EdgeBench, a scaling law for learning from environments emerges: - 📈 As agents interact with task environments over time, their aggregate performance is precisely fit by a log-sigmoid function. - 🧠 This phenomenon can be explained by an elegant theory of graph exploration. We are releasing an initial 51 of the 134 tasks, together with the full evaluation framework, to help advance long-horizon agent research. Check our blog & paper for more findings! Blog https://edge-bench.org/ Paper https://edge-bench.org/paper.pdf GitHub https://github.com/ByteDance-Seed/EdgeBench Dataset https://huggingface.co/datasets/ByteDance-Seed/EdgeBench Details below 👇🧵

Today, we give robots a /skills library that self-evolves and compounds indefinitely! Introducing ASPIRE: a robot solving its 100th task is no longer as clueless as solving its first. Coding agents observe multimodal sensory traces from simulation and real robots, launch an evolutionary search over control programs, and distill the best know-how into an ever-expanding library. ASPIRE is a new type of continual learning: "training" is skill refinement instead of gradient descent. "Trained model" is a repo of sensorimotor skills instead of floating weights. “Distributed training” is a panel of agents each practicing a different skill instead of sharded minibatches. Here's the beauty: ASPIRE gives the tired terms "sim2real transfer" and "cross-embodiment transfer" a whole new meaning. Bridging the sim-to-real gap is notoriously brutal. An end-to-end policy has to swallow both the visual shift (sim looks toyish next to a real camera) and the subtle contact physics it never quite gets right. ASPIRE sidesteps the mess, because it doesn't ship pixels or weights across the gap, but ships the know-how. The robot still has to practice in the real world, not zero-shot, but it gets there way faster because it isn't rediscovering the strategy from scratch. Same for going single-arm to bimanual hardware, which usually requires new data and retraining from zero. ASPIRE achieves up to ~10x cut in "transfer learning” tokens (yes, tokens are the new unit of *training* compute ;) Check out our gallery of 150+ tasks and 90+ skills the robots taught themselves, all on the website! Kind of wild that we can ship the "learned weights" as an HTML page rather than a GGUF. We'll open-source the full stack so your own robot library starts compounding from ours! Deep dive in thread:

在 TokenSpeed 推理引擎上对 Qwen3.5 的光速优化是一个重要的里程碑，在 NVIDIA GPU 上针对智能体工作负载实现了破纪录的每秒 580 个 token（tps）。 在 PyTorch Foundation 最新的社区博客文章中，你可以全面了解 Qwen3.5 模型在 TokenSpeed 推理框架中的完整设计、实现与优化，并亲眼看到这项工作如何提升性能 👉 https://bit.ly/4uGUvIS 这一成果是 @Alibaba_Qwen 推理团队、@lightseekorg Foundation TokenSpeed 团队、@NVIDIAAI 以及 Mooncake 团队共同努力的成果，其中 @tri_dao 为 FlashAttention-4（FA4）优化做出了特别贡献。@KVCache_AI