<a href="https:&#x2F;&#x2F;xcancel.com&#x2F;Fried_rice&#x2F;status&#x2F;2038894956459290963" rel="nofollow">https:&#x2F;&#x2F;xcancel.com&#x2F;Fried_rice&#x2F;status&#x2F;2038894956459290963</a>Related ongoing thread: The Claude Code Source Leak: fake tools, frustration regexes, undercover mode - <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=47586778">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=47586778</a>

Claude Code's source code has been leaked via a map file in their NPM registry

Axios compromised on NPM – Malicious versions drop remote access trojan

Related ongoing thread: Claude Code&#x27;s source code has been leaked via a map file in their NPM registry - <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=47584540">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=47584540</a>Also related: <a href="https:&#x2F;&#x2F;www.ccleaks.com" rel="nofollow">https:&#x2F;&#x2F;www.ccleaks.com</a>

The Claude Code Source Leak: fake tools, frustration regexes, undercover mode

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GitHub Monaspace Case Study

forkrun is the culmination of a 10-year-long journey focused on &quot;how to make shell parallelization fast&quot;. What started as a standard &quot;fork jobs in a loop&quot; has turned into a lock-free, CAS-retry-loop-free, SIMD-accelerated, self-tuning, NUMA aware shell-based stream parallelization engine that is (mostly) a drop-in replacement for xargs -P and GNU parallel.On my 14-core&#x2F;28-thread i9-7940x, forkrun achieves:* 200,000+ batch dispatches&#x2F;sec (vs ~500 for GNU Parallel)* ~95–99% CPU utilization across all 28 logical cores, even when the workload is non-existant (bash no-ops &#x2F; `:`) (vs ~6% for GNU Parallel). These benchmarks are intentionally worst-case (near-zero work per task) because they measure the capability of the parallelization framework itself, not how much work an external tool can do.* Typically 50×–400× faster on real high-frequency low-latency workloads (vs GNU Parallel)A few of the techniques that make this possible:* Born-local NUMA: stdin is splice()&#x27;d into a shared memfd, then pages are placed on the target NUMA node via set_mempolicy(MPOL_BIND) before any worker touches them, making the memfd NUMA-spliced. Each numa node only claims work that is already born-local on its node. Stealing from other nodes is permitted under some conditions when no local work exists.* SIMD scanning: per-node indexers&#x2F;scanners use AVX2&#x2F;NEON to find line boundaries (delimiters) at speeds approaching memory bandwidth, and publish byte-offsets and line-counts into per-node lock-free rings.* Lock-free claiming: workers claim batches with a single atomic_fetch_add — no locks, no CAS retry loops; contention is reduced to a single atomic on one cache line.* Memory management: a background thread uses fallocate(PUNCH_HOLE) to reclaim space without breaking the logical offset system.…and that’s just the surface. The implementation uses many additional systems-level techniques (phase-aware tail handling, adaptive batching, early-flush detection, etc.) to eliminate overhead, increase throughput and reduce latency at every stage.In its fastest (-b) mode (fixed-size batches, minimal processing), it can exceed 1B lines&#x2F;sec.forkrun ships as a single bash file with an embedded, self-extracting C extension — no Perl, no Python, no install, full native support for parallelizing arbitrary shell functions. The binary is built in public GitHub Actions so you can trace it back to CI (see the GitHub &quot;Blame&quot; on the line containing the base64 embeddings). Trying it is literally two commands:<pre><code> . frun.bash 
 frun shell_func_or_cmd &lt; inputs
</code></pre>
For benchmarking scripts and results, see the BENCHMARKS dir in the GitHub repoFor an architecture deep-dive, see the DOCS dir in the GitHub repoHappy to answer questions.

Show HN: Forkrun – NUMA-aware shell parallelizer (50×–400× faster than parallel)

Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We&#x27;d already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector&#x27;s main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn&#x27;t provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We&#x27;d need a small team of sharp engineers and 6-12 months, I figured. And we&#x27;d probably still fall short of the performance of a mature system like Parade&#x2F;Tantivy.Or would we? I&#x27;d be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I&#x27;ve been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I&#x27;m thrilled&#x2F;exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:<a href="https:&#x2F;&#x2F;github.com&#x2F;timescale&#x2F;pg_textsearch" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;timescale&#x2F;pg_textsearch</a>In the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade&#x2F;Tantivy&#x27;s query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:<a href="https:&#x2F;&#x2F;www.tigerdata.com&#x2F;blog&#x2F;pg-textsearch-bm25-full-text-search-postgres" rel="nofollow">https:&#x2F;&#x2F;www.tigerdata.com&#x2F;blog&#x2F;pg-textsearch-bm25-full-text-...</a>It&#x27;s exciting (and, to be honest, a little unnerving) to see a field I&#x27;ve spent so much time toiling in change so quickly in ways that enable us to be more ambitious in our technical objectives. Technical moats are moats no longer.The benchmark scripts and methodology are available in the github repo. Happy to answer any questions in the thread.Thanks,TJ (tj@tigerdata.com)

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