标签：#m

This week's releases split neatly into two categories: useful incremental hardening (uv, GitLab, Copilot) and things that should change how you architect systems today (Spring CVEs, pg_durable, and a Cornell paper that quietly invalidates a lot of RAG assumptions). The Spring security cluster alone is enough to justify a dependency audit before the weekend. uv 0.11.19 adds CPython 3.15 beta support uv now always computes SHA256 checksums for remote distributions—previously this was situational—and adds PyEmscripten platform support per PEP 783, which formalizes Python packaging for browser and WASM targets. CPython 3.15.0b2 is available as a managed runtime, and a cross-platform installation edge case on Windows hosts has been resolved. The SHA256 change is the one worth noting for security posture. Making verification unconditional rather than optional closes a gap where distribution integrity could go unchecked depending on resolver path. The PyEmscripten addition matters if you're packaging Python for browser runtimes—previously you were working around the absence of a formal platform tag; now you're not. Verdict: Ship. Drop-in upgrade, no breaking changes. If you manage Python distributions or target WASM, update now. Everyone else should still update—supply-chain hardening by default is worth the two minutes. GitLab 19.0 adds group-level review instructions, secrets manager GitLab 19.0 ships two meaningful additions for teams: group-level custom review instructions for Duo code review, configured via .gitlab/duo/mr-review-instructions.yaml with cascading inheritance across projects, and a Secrets Manager that exits closed beta for Premium and Ultimate tiers. Group-level review instructions solve a real annoyance—if you've been maintaining per-project AI review configuration across a monorepo organization, you can now centralize that and let projects inherit or override. It's the kind of change that sounds minor until you've had to sync a guideline update across

AI-disclosure: AI-assisted draft, human-reviewed. The demo numbers are the verbatim stdout of a deterministic, stdlib-only Python script included in full below — re-run it and you get the same bytes. The attempt counts in that script are a SYNTHETIC fixture I chose to exercise the accounting mechanism, calibrated to the retry skew I see in my own scraper logs (run counts from my Apify history). It is NOT a benchmark of any named vendor's API or prices. The one external claim (the cost-per-successful-task formula) is attributed and linked. The cheaper API was 2.5x cheaper per call. The monthly bill came in higher anyway. Not by a rounding error. The "cheap" option cost 1.63x more per successful task than the one with the bigger sticker price. Same workload. The price page never showed me that number, because the price page doesn't know your success rate. You do — after you've already paid. This is the arithmetic the per-call price hides. And it's a decision you make before you spend, not a cap you bolt on after. TL;DR You compare two API tiers by per-call (or per-token) price and pick the cheaper one. That ranking can be wrong. You pay for every attempt — success or fail. The denominator that pays the bills is successful tasks , not calls. True cost = price_per_attempt × attempts ÷ successes . A cheap tier with a low success rate burns its discount on retries. In the run below: cheap tier $0.0020 /call but $0.0096 /success; robust tier $0.0050 /call but $0.0059 /success. The sticker winner loses . For anyone choosing an API, model, or tier for an agent: log attempts and successes for a week, divide, then decide. A 70-line script is at the bottom — drop in your numbers. The price page is a sticker, not a bill Here's the trap, stated plainly. The number on the pricing page is per call . The number on your invoice is per call too — but the value you got is per successful task . Those are different denominators, and the gap between them is exactly the work that failed. E

This week's AI tooling news splits cleanly between infrastructure you can ship today and capability bets that require more careful evaluation. Anthropic dropped two significant releases—Fable 5 and Managed Agents updates—while the Workflow SDK landed a cancellation primitive that eliminates entire categories of homegrown plumbing. Underneath all of it, a sharp incident review from Anthropic is the most practically useful thing published this week if you're running multi-turn agents in production. Workflow SDK adds AbortController cancellation support The Workflow SDK now threads AbortSignal through workflow steps, using the same web-standard API you already use with fetch . Pass an AbortSignal into your workflow, inspect it inside steps, and you get cooperative cancellation that survives durable suspension and replay. This matters because cancellation in long-running workflows has historically required custom infrastructure—timeout flags passed through context, manual cleanup hooks, bespoke race logic. That's not interesting code to write or maintain. With AbortController support, you get timeout steps, request racing, and parallel work cancellation with patterns your team already knows. Two important caveats: this requires workflow@beta , and cancellation is cooperative. The runtime won't forcibly terminate a step—your step code needs to inspect the signal and respond. If you have steps with opaque third-party calls that don't accept signals, you're still writing wrapper logic. Verdict: Ship. If you're on Workflow SDK 5 and running long-horizon workflows with timeout or race requirements, upgrade and wire this in now. The pattern is standard, the boilerplate reduction is real, and there's no meaningful downside if your steps are already structured around explicit control flow. Anthropic adds dreaming, outcomes to Managed Agents Two distinct additions here. Outcomes let you define explicit success criteria enforced by a separate grader agent—replacing manual prompt

It's unclear whether the system is currently intact.

This week's tooling news splits cleanly between performance and compliance: a Go Protobuf parser that closes the gap between reflection and generated code, and a GitLab update that finally makes air-gapped AI deployments practical. Layered in are a forced AWS migration, a cost-pressure move in reasoning model pricing, and an Elasticsearch alternative picking up serious enterprise backing. Here's what's worth your attention. hyperpb Dynamic Parser Matches Generated Code Speed hyperpb is a runtime-compiled Protobuf parser for Go. You feed it a schema at startup, it runs an optimization pass, and the result is a compiled message type you can reuse across requests. Benchmarks show 10x faster parsing than dynamicpb and roughly 3x faster than hand-written generated code. The implication for generic Protobuf services—brokers, validators, schema registries—is significant. If you're doing broker-side validation today with dynamicpb , you're likely throttling throughput or skipping validation under load. hyperpb removes that tradeoff. The catch is that compiled types require caching (the optimization pass is slow and should not run per-request) and field access remains reflection-only—you're not getting struct field ergonomics. Verdict: Ship. If your validation pipeline is hitting dynamicpb throughput limits, this is a drop-in replacement for the hot path. Cache your compiled message types at initialization, and profile field access patterns before assuming it fits your read-heavy workloads. Quickwit Joins Datadog, Relicenses to Apache 2.0 Quickwit, the Rust-based petabyte-scale log search engine, has been acquired by Datadog and relicensed from AGPL to Apache 2.0. Development continues as open source. Distributed ingest and cardinality aggregations are on the near-term roadmap. The production credibility is already there—Binance runs 1.6PB/day through it, Mezmo has petabyte-scale logs in production. The Apache 2.0 relicense removes the corporate control concern that kept som

This week's tooling landscape is quieter on the AI-native side but dense with infrastructure moves that affect how AI-driven workloads actually run in production. Cloudflare's Workflows scaling overhaul is the clearest signal: agent-triggered execution is now an assumed pattern, not a novelty, and platforms are rearchitecting accordingly. The rest of the week rounds out with a kernel maintenance drop, a meaningful abstraction removal in tRPC, and a Biome beta that's finally making ESLint replacement feel plausible. Linux 7.1 Released with Driver and Networking Fixes 7.1 is a maintenance release. No architectural changes, no new subsystems—just patches you should care about if you're running affected hardware or kernel-adjacent tooling. The two fixes worth flagging are heap overflows in the USB serial io_ti driver ( get_manuf_info() and build_i2c_fw_hdr() ), plus memory leak corrections scattered across drivers and networking subsystems. Trace tooling also gets updates, which matters if you're doing kernel-level performance analysis on production systems. One operational note: Torvalds is traveling, so merge window latency may be irregular. If you're tracking pull request timelines for custom kernel builds, plan for slippage. Verdict: Ship — if you're on 7.0 and running USB serial hardware or affected networking paths, upgrade on your normal kernel cycle. No breaking changes, no new dependencies, nothing to validate beyond your existing regression suite. tRPC Drops Abstraction Layer for React Query This is the kind of change that looks small in a changelog and feels large in daily development. The new tRPC client exposes native TanStack Query interfaces— QueryOptions and MutationOptions —directly, rather than wrapping them in tRPC-specific hooks. The practical effect: if you're already using TanStack Query elsewhere in your app, you stop context-switching between two similar-but-different mental models. You call .queryOptions() and .mutationOptions() factories and pa

Doomscrolling on social media is a very common problem and can feel like an endless loop of constantly losing track of time. Instead of tackling this issue with strict limits, a new mindful screen time app called Mivo Scrolling is trying to shake things up. Launched last month, Mivo doesn’t look super different from other […]

“You would not believe the texts I got from these tech guys,” NYT reporters Maggie Haberman and Jonathan Swan quote Donald Trump as telling associates in an upcoming book.

FERC told grid operators to give data centers a fast lane for interconnections, but it failed to address electricity supply shortages.

submitted by /u/CircumspectCapybara [link] [留言]

For the past four years, a sprawling Android-based botnet called Popa has forced millions of consumer TV boxes to relay Internet traffic linked to advertising fraud, account takeovers, and mass data-scraping efforts. This week, researchers from multiple security firms concluded that the Popa botnet is linked to NetNut, a "residential proxy" provider operated by the publicly-traded Israeli firm Alarum Technologies Ltd [NASDAQ: ALAR].

Do you often find yourself shopping at the big blue, or perhaps you’re just looking for an alternative to Amazon? Either way, Walmart is currently offering a year of its Walmart Plus subscription for $49, which typically costs $98. The membership includes perks like free delivery on orders over $35, free next-day and two-day shipping […]

Apple TV will show Formula 1's Australia Grand Prix for free, even to non-subscribers.

“People just really want to take back control of their time, their lives, their attention... They’re down for whatever helps them do that.”

Biggest AI firms will likely recoil at Bernie Sanders' AI wealth fund.

The Onion's InfoWars officially has a launch date: On July 2nd, the conspiracy network previously run by Alex Jones will return as a comedy and media platform. The reboot comes more than a year and a half after news broke that the satirical news site was working to acquire the property owned by Jones, a […]

submitted by /u/craigkerstiens [link] [留言]

Thinking about indexing, we know a only a big picture that uses B-Tree structures in memory, but in modern DBMs systems there are some enhancements introduced in indexing where systems like PostgreSQL are utilizing some new OS system calls like io_uring to make syncrhonous IO reading, also direct retrieval of record from disk using functions like fseek() and in memory optimization of traversing like applying binary search on leaf page. PostgreSQL always keeps a file for indexing, unlike MySQL where it only keeps an indexing file for non-clustered indexing, and clustered indexing is calculated directly from the table, it is interesting to study the indexing file structure as well Index file structure has line pointers, TID called as tuple ID Tuple is the column value, the same column you registered for indexing, by using which Postgres calculates the actual physical address of the records from the disk to fetch it directly. Postgres has another interesting feature, while creating an index, it always sorts data and maintains ranges of pages in page 0 of the indexing, where Postgres can identify which page to look for and then load that page into memory, and then it becomes a leaf page, then binary search is performed to get the actual TID so that the record can be fetched directly For an in-depth explanation about the index file structure, loading the index file into memory, and then searching for the actual record's address, check out the given link submitted by /u/Ok_Stomach6651 [link] [留言]

The software engineers filed a complaint with Seattle’s civil rights office accusing Amazon of illegally retaliating against them for expressing their personal political beliefs.

I had always wondered, why ";"? And the number of times, I have been frustrated because of this little guy, missing it somewhere, adding an extra somewhere, I really wanted to learn about it. So today I did, and I thought I might share it with you guys! Story of Semicolon. submitted by /u/Mastbubbles [link] [留言]