Kubernetes Swap, ReviewBench, OpenRadioss Fork

Simon Willison replicated Colin Frasier's experiment testing LLMs on "compute the sum but return the answer in words" across digit lengths. Frasier's original GPT-4o run showed accuracy collapsing toward zero as operand digit counts grew beyond ~5–6 digits. Willison reran the test locally on a DGX Spark with Qwen3.8-27B-Q4_K_M.gguf, 30 fixed pairs per digit-length cell (n=5,070). With reasoning disabled, the model scored only 23.57% overall, degrading rapidly with more digits. With reasoning enabled (one pair per cell, 169 total, slow to run), it answered 167/169 correctly. Reasoning traces show explicit column-by-column addition with carries. Limitation: the reasoning run used only one sample per cell, so results may not be reproducible.

Siemens quietly deleted the OpenRadioss repository (GitHub page remains), redirecting users to its Simcenter Radioss transition page, after acquiring Altair last year. OpenRadioss was the open source release of Altair's Radioss finite element solver for crash, blast, and impact simulation, released in September 2022. Brian Clemens (Rocky Linux co-founder) launched a fork, OpenCourant, carrying the full commit history under AGPLv3 — the license legally permits forking despite the takedown. The proprietary Siemens-hosted build pipeline was rebuilt from scratch; Linux and Windows x86_64 packages are already shipping. A closed-source hm_reader input-reader binary lost with the repo was recovered from a community copy, verified, and included in builds. The project is an independent community effort seeking past contributors, needs release archives to add ARM64 support, and packages Starter/Engine builds in single/double precision with SMP and converters.

Ubuntu 26.10 "Stonking Stingray" includes Sequoia PGP, a Rust-based OpenPGP implementation, preinstalled from the main archive alongside GnuPG. Sequoia was started in 2017 by former GnuPG developers, designed as a library with sq (encryption, signing, key management) and sqv (signature verification) as CLI frontends, replacing gpg/gpgv roles. It implements RFC 9580, the 2024 OpenPGP revision, while GnuPG continues from RFC 4880 and its own LibrePGP extensions. Typing gpg/gpgv still routes to GnuPG; both implementations currently coexist. Replacing GnuPG as default is a stated future goal, not yet done, with no timeline given by Canonical. Precedents (coreutils transition reaching 100% in 26.10, sudo-rs) took multiple release cycles before becoming defaults. Landing in the main archive is only the first milestone; whether Sequoia becomes default remains uncertain.

The post is a promotional page (only a title, URL, and blurb) describing OpenAI's approach to text watermarking under EU provenance rules. It states that OpenAI outlines where watermarks apply, how detection works, and why access to detection starts with researchers. No technical details—watermarking scheme, coverage, detection accuracy, or limitations—are present in the supplied content. The actual mechanics cannot be summarized beyond these three high-level topics.

GitHub released ReviewBench, an open offline benchmark for evaluating AI code review agents. It contains 219 public pull requests from 187 repos across 19 languages, sampled from analysis of 103.9M GitHub PRs, with language and repo-size distributions matching GitHub overall but PR sizes weighted toward substantive multi-file changes. Ground truth is a multi-source golden set combining human reviews, follow-up-commit-inferred issues, static analysis, and frontier LLMs, deduplicated semantically and validated under a shared rubric; findings are labeled by severity and category. Scoring uses six metrics in two families: grounded precision/recall/F1 against the fixed golden set, and augmented metrics where an LLM judge (Claude Sonnet 5) independently scores unmatched findings so new valid discoveries earn credit. Results are sliceable by severity, category, and precision–recall preference via a configurable Fβ score that re-ranks the leaderboard. Trust chain includes a published rubric, calibrated grader, and independent senior-engineer re-labeling agreeing 96.6%; dataset, judge, and matcher are versioned and published. GitHub reports offline ReviewBench movements have matched production A/B outcomes for Copilot code review, e.g., a multi-model ensemble experiment predicted and showed +8.0% precision, +13.6% recall, +61% comment volume, and −8% cost per review. Limitations: small 219-PR corpus, recall comparisons rely on grounded metrics since augmented recall's denominator is agent-dependent, and leaderboard submissions are maintainer-approved.

GitHub launched ReviewBench, an open offline benchmark for AI code review agents, built from a corpus of 219 public pull requests across 19 languages modeled on 103.9M real GitHub PRs (PR size skewed toward multi-file changes). Ground truth uses a multi-source golden set (human reviewers, follow-up commits, static analysis, frontier LLMs), semantically deduplicated and validated under a published rubric by a Claude Sonnet 5 LLM grader. It reports grounded and augmented precision/recall/F1: augmented metrics let unmatched findings earn credit via independent judging, but grounded recall is the headline cross-system metric since augmented recall denominators vary per agent. Results can be sliced by severity and category and re-ranked with adjustable Fβ weighting between precision and recall. Independent senior-engineer relabeling of golden true-positives agreed 96.6% with the benchmark; dataset, judge, and matcher are versioned and publicly available with a self-serve runner. Internally, offline ReviewBench results for Copilot code review tracked production A/B direction, e.g., a multi-model ensemble predicted and showed +8.0% precision, +13.6% recall, and −8.0% cost per review. Limitations: the corpus is small (219 PRs), augmented metrics are per-system diagnostics rather than comparable scores, leaderboard publication is maintainer-gated, and validation is internal rather than third-party audited.

GitLab's internal Django GRC tool originally used only login_required, which let authenticated Security Compliance users access Internal Audit data—a login-vs-authorization gap. They restructured into a shared core (auth, user management, audit logging, base UI) with two independent module apps (Security Compliance, Internal Audit), each owning its models, views, and APIs. Constraint: core knows nothing about module data; modules depend on core but never on each other. Access control uses Django's built-in groups: each module maps to one group, enforced independently in views, REST API (DRF permission class), and navigation. Implementation centers on one core GroupRequiredMixin that raises PermissionDenied; each module adds a three-line subclass setting required_group_name. A user_in_group template filter hides nav links but is explicitly a UX guard, not a security boundary—URLs must still be blocked server-side. New modules inherit the pattern with no new enforcement logic; limitations noted include that hiding UI links alone is insecure and users may belong to both, one, or no groups.

NPR reports on a man who, after declaring bankruptcy and being self-excluded/banned from sports betting sites, resumed gambling through Kalshi, a CFTC-regulated prediction-markets platform. Kalshi operates as a federally regulated event-contract exchange, so it bypasses the state-level self-exclusion lists and responsible-gambling databases used by sportsbooks. This highlights a regulatory gap: harms controls tied to state gambling law don't cover federal prediction markets, which offer similar high-frequency wagering on sports and other events. The story is part of broader scrutiny of Kalshi's rapid expansion into sports-adjacent betting markets. Critics argue it undermines problem-gambling protections; the piece raises addiction and consumer-protection concerns.

Terry Tao published a blog post titled "The Future of Mathematics" on his WordPress blog on October 5, 2026. The submission provides only the article URL and a Hacker News discussion link, not the article text. No details of the post's content, arguments, or technical claims are included in the supplied data. The submission had 94 points and 50 comments on Hacker News at time of capture.

A Hacker News submission linking to Terence Tao's blog post "The Future of Mathematics" (terrytao.wordpress.com, Oct 5, 2026). No article content is included in the post data, so the blog's specific claims cannot be summarized. The submission earned 112 points and 69 comments.

HN post (477 points, 152 comments) linking Reflection AI's announcement of Beam, described as a 501B-parameter open-weight model. The supplied content contains only the link and metadata, not the blog post itself, so no technical details (architecture, training, benchmarks, license) can be verified. Discussion is available in the linked HN thread.

Kubernetes node swap reached GA in v1.34, letting nodes page out idle anonymous memory to fast NVMe Local SSDs to raise pod density for memory-heavy, bursty workloads like agentic AI sandboxes. Previously swap was avoided because cgroup v1 merged memory and swap accounting; cgroup v2's separate swap tracking plus fast SSDs resolve this. Benchmarks: a Linux 6.1.1 kernel CI build cut its memory limit 50% (600 MB→300 MB) with no slowdown (374s vs 433s), but compressing to 200 MB forced active working set into swap and added >40% runtime. Headless Chrome density rose from 80 to 160 pods with gVisor (+100%) and 40 to 50 with Kata (+25%); unsandboxed runc scaled from 512 failures to 768 pods. Python sandboxes (~375 MiB each, MovieLens 20M) scaled 3×, from 80 to 240 concurrent sessions per node. At peak density, latency growth came mainly from CPU contention, not swap I/O. Usage: set kubelet failSwapOn: false with memorySwap.swapBehavior: LimitedSwap (e.g., GKE Node Memory Swap on Local SSD), and run Burstable QoS pods with memory limits above requests. Key caveat: swap is a buffer for dormant/burst memory, not a substitute for RAM covering active working sets.

Oracle implemented FETCH FIRST (SQL:2008, added in 12cR1) by rewriting it to an analytic ROW_NUMBER() query. Since 23.1 (fix control 35915968, visible in 26ai), the simple FETCH FIRST n ROWS ONLY case is instead rewritten to a ROWNUM <= n filter over an ordered subquery, shown via plan (COUNT STOPKEY) and DBMS_UTILITY.EXPAND_SQL_TEXT. Motivations: the analytic rewrite historically costed poorly (fixed by first-k-row optimization, fix 22174392, WINDOW NOSORT STOPKEY in 19c), and the rewrite caused a wrong-results bug — a query combining EXISTS and NOT EXISTS over a view with OUTER APPLY, ANSI join, and correlated FETCH FIRST could return a row despite the contradiction. 10053 traces show the two rewrites make different view-merging decisions; the ROWNUM path merges wrapper blocks while keeping the row-limit boundary and avoids the bug. Limitations: OFFSET, WITH TIES, and percentages still use other paths (control 35969400 can yield a native ROW LIMIT operation); the root cause in Oracle's code wasn't identified, only that enabling 35915968 avoids the wrong result. PostgreSQL, by contrast, implemented FETCH FIRST natively as LIMIT/OFFSET syntax with a Limit node and first-k costing from the start.

Keyset pagination avoids OFFSET's read-and-discard cost by seeking after the last row of the previous page. Use a SQL row comparison like WHERE (created_on, id) < (:last_ts, :last_id) with ORDER BY on both columns and a unique tie-breaker (id). On PostgreSQL, a correctly typed row comparison becomes a full composite Index Cond, seeking directly to the B-tree position (index-only scan, 50 rows). Expanding it to the equivalent OR expression is logically identical but PostgreSQL keeps it as a Filter, potentially scanning many index entries; simpler AND/OR variants are too broad, too narrow, or drop rows. A datatype mismatch (comparing a bigint id to numeric) silently downgrades the plan to a lossy condition: only created_on <= ... is pushed to the index, with the row comparison rechecked as a filter. Fix by casting the parameter (not the column) to match the column types, ideally via a prepared statement that pins parameter types. Support varies by database: PostgreSQL and MySQL (with limits) use the row comparison as an index boundary; YugabyteDB needed a scalar ts bound workaround; Oracle and SQL Server lack the syntax and need scalar predicates or multi-range seeks. Always verify with the execution plan: check Index Cond vs Filter, rows removed, and buffers rather than trusting syntax.

Claude Statuspane (open source, MIT, by Anji Xu) adds a floating status card above the Claude Code prompt showing model, reasoning effort, context-window usage, 5-hour and 7-day rate limits with reset countdowns, git info, and running session cost. It uses Claude Code's new early-access mod system (plugin-drawn UI), gauges shift to warning color at 60% and error at 85%, and a /compact button lets developers reclaim context before hitting limits. Requires Claude Code 2.1.287+, a 70+ column terminal, and doesn't run in the desktop app; it also follows themes and collapses to a single button. GitHub CI integration shows the latest Actions run per branch via the signed-in GitHub CLI, polling every minute (10 seconds during active runs), with push/merge-triggered runs pinned for 10 minutes after completion. A progress API lets any script drop JSON files (label, percentage, TTL, state) into a folder; the card shows the 20 most recent files, refreshing every second. Security guardrails include 64 KB JSON caps, stripping control/bidirectional/zero-width characters, all-local execution, and CI lookups via the user's own GitHub CLI credentials. Limitations: brand-new with little adoption, depends on an early-access API that may break, and it offers per-developer visibility only — no central, auditable governance of agent spend or pipeline outcomes.

Cassidoo's weekly newsletter (Oct 9, 2026) opens with web links on the history of port numbers, Evil Martians' Gatsby-to-Astro migration done without rewriting React components, new HTML/CSS design-system features, AI's "terminal era," and bidirectional text handling in LLMs. The author highlights a blog post on accessibility, arguing that AI automation has renewed interest in accessibility and speculating about a future internet pricing model that separates human and bot traffic. The interview question is a grid BFS problem: zombies (2) infect adjacent living people (1) each minute; return minimum minutes until no people remain, or -1 if unreachable (multi-source BFS). Community submissions to last week's "temperature drops" question are linked from GitHub, CodePen, Bluesky, and other sources. No technical code changes or releases are described; the post is a link roundup, essay teaser, coding puzzle, and general updates.

Cloud Run's custom scaling controls went GA on 29 Sep 2026 (preview 16 Apr 2026), letting owners replace the fixed 60% CPU and concurrency targets via --scaling-cpu-target (0.10–0.90) and --scaling-concurrency-target (0.10–0.95); either can be disabled but not both. Instance count is computed from three drivers—CPU, concurrency, and adaptive concurrency (which drops an instance's concurrency by 1 when CPU exceeds 90%)—and the highest number wins. Lower targets scale earlier at higher cost; higher targets create a wider dead zone and step changes. The concurrency target is a fraction of max concurrency, not a replacement. Billing matters: request-based billing only charges CPU during requests, while instance-based billing charges full instance life (except scale to/from zero). Limitations: no scaling on memory, latency, or queue depth; not available for jobs or worker pools; max instances still queue then 429 and can be briefly overshot.

The author hosts WordPress sites on Pressable and highlights its one-click "WordPress MCP Adapter" toggle in the dashboard. With that enabled (plus Jetpack/WordPress.com), an MCP-capable agent like Claude Code can connect and perform actions such as creating draft posts or reviewing existing drafts. Demonstrated use cases include checking a post for mistakes and enforcing formatting rules; the author suggests pairing it with a SKILL.md style guide (e.g., only touch drafts, label code blocks, self-host images, validate links). Limitations: the post is anecdotal, relies on Pressable/Jetpack hosting, and the author only endorses rote formatting tasks, not AI-generated writing.

WebKit Igalia Periodical #80 covers work from Sept 28–Oct 5. Initial OpenSSL crypto backend support landed for WPE/WebKitGTK, still in development and not production-ready. Moving-steps handling added for map, img, label, and radio inputs, including custom element registries. Skia compositor now correctly stacks backdrop-filter effects. LBSE improvements: reduced save/restore pairs per SVG shape (cutting GPU-process IPC from ~8.5 to ~6.5 messages/shape), fixed objectBoundingBox clipPath transforms, non-scaling-stroke hit testing/repainting after transform changes, filter source areas on partly scrolled layers, nested overflow clipping, and mispositioned child layers under transformed ancestors. WPE legacy API regained DRM vertical blank pacing after two regressions had forced a fixed 60 Hz timer fallback.

RemoveMacAI is an open-source CLI tool that deletes Apple Intelligence models and disables its features on macOS, freeing up to 12GB of disk space. It restores what the old single Settings toggle did before that toggle was removed in macOS 27, when features became split across multiple settings panes. The developer says users must manually find about a dozen settings, several under Screen Time, which the tool automates. Reported via Reddit and spotted by MacRumors. Limitations: no details given on reversibility or full feature coverage.

OpenAI is rolling out "textGrain," an invisible, machine-readable text watermark, to ChatGPT and Codex, initially only for EU users. The move aligns with EU AI Act transparency requirements. OpenAI claims textGrain matched or exceeded rivals like Google DeepMind's SynthID-Text (also used by Anthropic). Benchmarks show similar performance for watermarked vs. unwatermarked text. However, OpenAI notes textGrain does not guarantee detection, and the addition drew some user backlash.

Nvidia raised the Shield TV Pro price by $100 to $299.99 effective October 2, citing increased component costs tied to a memory shortage. The 2019-era device launched at $199.99 with a Tegra X1+ processor, 3GB RAM, and 16GB storage. The price hike mirrors increases across other devices like cameras and consoles. Details on the entry-level Shield TV (2GB RAM, 8GB storage) were cut off in the source.

The 2026 M6 Mac Mini (256GB storage, 16GB RAM, 12-core CPU / 12-core GPU) is on sale at Amazon for $779.99, down from $899 — the model's first price drop since launch. The M6 chip is roughly 20% faster than the M4, though the design is largely unchanged and the base price rose from $599 to $899. For comparison, Best Buy restocked the M4 Mac Mini with the same storage at $799, making the discounted M6 the better buy.

This post is part 2 of the Effect "Cluster" module series. It covers writing a first entity and tracing one of its messages across the cluster. It explains how runner nodes reach consensus on entity ownership (message routing to the responsible node). The source contains no further technical details, code, or benchmarks.

A Reddit post in r/webdev notes that the Example.com website has changed again. The post consists of an image screenshot; no textual details about the changes are provided. Example.com is the IANA-reserved domain used in documentation and examples. The specific nature or extent of the changes cannot be determined from the available content.

This post is only a Reddit link submission to an external blog article titled "Junior Developers Can No Longer Write a Working Function Without a Chatbot." No article body or technical details are included in the provided content. The title suggests a claim that junior developers increasingly rely on chatbots to write working functions. No evidence, data, or implementation details are available to verify or summarize further. The submission is by /u/histoire_guy in r/webdev, linking to blog.pixlab.io.

NetworkChuck splits Mike's flat home network into multiple VLANs using OPNsense as the firewall. IoT devices (potential attack entry points like smart fridges) are quarantined on a separate network with deny-by-default firewall rules and an alias. Work and user networks get their own gateway IPs and DHCP ranges on the same OPNsense mini PC. A QNAP QSW-L2110-10T lite-managed switch handles trunk (tagged) and untagged ports for the VLANs. Setup was complicated by a hidden DHCP checkbox in OPNsense's Dnsmasq configuration, tracked down via Claude Code. An AI agent (Pig, running Hermes on a Raspberry Pi) was also used to work with the switch, which lacks a CLI. Limitations: video is sponsored by Flare and is part two of a series, so it presumes an OPNsense box from part one.

A Kong AI Gateway video demonstrates automatic failover between LLM providers. It uses the gateway's Priority load balancing algorithm to organize models into priority groups. When all targets in the preferred group are unavailable, traffic falls back to a backup group. The goal is resilience against LLM provider outages. Setup details and configuration are documented at developer.konghq.com/ai-gateway/load-balancing/. The source offers only a high-level overview, with no concrete metrics or benchmarks.

A tutorial video by Traversy Media on building a practical CI/CD pipeline. It explains CI/CD and GitHub Actions, then walks through a starter repo (github.com/bradtraversy/cicd-lab-starter): adding a verify script, pushing to GitHub, and deploying to Render. The workflow runs automated checks on pushes, with enforced guardrails/gate tests on both GitHub and Render, and demonstrates preview environments. The video is sponsored by Render (promo credit code) and links to a Devstash page with all commands. Content is limited to the description; no depth on specific implementation details.

Video walkthrough of a real AI engineering interview question: diagnosing and reducing chatbot response latency. Covers root cause analysis of slow responses as the first step. Discusses optimizing database queries as a key latency reduction technique. Includes a follow-up question segment extending the discussion. Content is an ~6 minute educational video, not a technical writeup; detailed implementation specifics are only in the video itself.

A team found that modifying a ~20-token suffix in a 15,000-token prompt caused OpenAI prompt caching to return zero cached tokens, wiping out their Codex quota. OpenAI's prompt caching uses automatic prefix caching with cache breakpoints, so any change to the end of the prompt invalidates the entire cached prefix. The key takeaway: keep prompt suffixes stable and put volatile content early or avoid appending changing tokens to preserve cache hits.

The post describes a production incident where AI agents executed every Kafka task twice due to a single Kafka configuration setting. The core issue is that Kafka's consumer/session defaults assume fast millisecond handlers, while each agent task took roughly six minutes. Long processing caused consumers to exceed timing thresholds, triggering rebalances/re-delivery and duplicate execution. Duplicates inflated compute costs, which the author describes as a surprisingly large bill. The article is paywalled; only a brief snippet is available, so specific setting names and the exact fix are not stated in the accessible content.

A Snowflake Builders Blog post compares an AI analyst answering questions on Snowflake data with and without a semantic model, illustrating how LLMs can produce confident but incorrect answers. It frames the experiment within the broader debate about AI capability claims. The full article content is not included in the source feed, so detailed technical specifics of the setup and results are unavailable.

The post describes an AI-written Drizzle ORM migration that broke the schema history. The root cause was missing migration snapshots, which caused Drizzle Kit to fail when generating subsequent migrations. The team resolved it by resetting the migration baseline and added guardrails to prevent recurrence. The linked Medium page is paywalled/preview-only, so deeper implementation details are unavailable.

Raw SQL bodies ending in a -- line comment previously produced invalid DDL: the planner interpolated them into larger statements, so the comment swallowed the rest of the line (e.g. the closing paren of CHECK (...)). Fix: a typed OpaqueSql AST node for unparsed SQL text, with a single renderOpaqueSql function that appends a trailing line break when the text contains --; renderers for CHECK constraints, policies, indexes, column defaults, and ALTER COLUMN TYPE USING all go through it. Bodies without -- render byte-identical, so no existing statements change. Wire names (hashes of normalized SQL) now keep one line break per line when the body contains --, since a break ends a comment and is semantically meaningful; other whitespace is still collapsed, and all existing names are unchanged. Bodies with both a comment and a line break get a new object name once; the next migration plan drops and recreates the object. Committed contract-free migration factories (fn, checkExpression, etc.) still accept strings for compatibility; extension authors now construct nodes via opaqueSql(text). Rationale: stripping comments was rejected because Prisma doesn't parse SQL (can't distinguish -- in string literals), and duplicating the newline rule across render templates was error-prone. Covered by integration tests on PGlite/SQLite plus naming and rendering tests; three tarball tests couldn't run locally and are left to CI.

Effect 4.0.1 is a patch release with fixes across core, cluster, RPC, HTTP, and MCP modules. AI/language-model: Anthropic system instructions are fully preserved, mid-conversation system messages are supported, and LanguageModel.supportsSystemMessagesInHistory allows querying support with scoped overrides; per-tool response schemas are now cached to reduce schema construction in generateText/streamText. Caching/concurrency: interruption in Effect.cached*, Cache, ScopedCache, RcRef, RcMap is treated as abandonment — concurrent callers share one computation interrupted only when all are interrupted, interrupted results are never cached; cachedInvalidateWithTTL types now accept Exit-based TTL callbacks; ScopedCache sets expiry before waking waiters; Pool TTL only expires idle unreserved items. Cluster: entity registration is blocked on shard-ownership loss, workflow replay avoids duplicate dispatches, PostgreSQL deadlocks between message claims and shard resets are fixed via row-lock ordering, and shard lock refresh/singleton reassignment no longer cancel or release in-flight work. Core/runtime: fibers preserve AsyncLocalStorage context across wakes/interruptions, pending interruptions survive uninterruptible failure, Stream.scan emits initial state on empty streams, Stream.throttle "shape" avoids rate drift using monotonic time, and schedules pause during idle aggregation. Other fixes: HashRing shard assignment is deterministic (ordered weight summing, tie-break by node key, with note that fractional-weight/tie assignments may change), HttpStaticServer honors If-Range, B3/W3C trace context decoding fixed, HttpApiSchemaError no longer reported as server failure, JSON Schema pattern importing handles canonicalized RegExp, and MCP fixes cover optional field encoding, stateful capability preservation, and SSE keepalives on Cloudflare Workers.

Prisma v8.0.0-rc.14-dev.25 (TML-3420, PR #30559) closes out the storage-naming project via ADR 258. Storage names are now verbatim: a model like UserProfile maps exactly to the table "UserProfile" unless @@map/@map overrides it. Changing a storage name is an explicit rename operation the author states (e.g., this.renameTable); the migration planner cannot infer it. The planner still rejects drop-and-create changes where names differ only in the first-letter case; MIGRATION.TABLE_NAME_CASE_CHANGED is a transitional check. Docs updated: Data Contract doc gains a "Storage names" section; Migration System doc records renameTable and the planner's refusal. The transient project directory projects/psl-verbatim-table-names/ was deleted, with durable content moved to docs/. ADR 258's alternatives section retains three rejected designs; the planner-hint approach remains the intended future direction. Limitation: renames update only eligible planner-named objects; explicitly named or migration-changed objects keep their database names.

Storybook 11.0.0-alpha.2 is a pre-release with broad fixes across core, CLI, and addons. Addon Vitest now restarts a dead test runner, sends the story index via IPC instead of per-frame, and blocks setProjectAnnotations with auto-migration of vitest.setup.ts. Addon Docs keeps the dev server alive when story files are deleted during rebuilds; Addon Links handles path-only story URLs. Breaking/cleanup changes: argTypes defaultValue removed, argTypes dropped from loader/play/beforeEach/afterEach contexts, --preview-url and --force-build-preview CLI flags removed, TypeScript 5.9 and React 18 now required, legacy decorator feature flags dropped. Svelte CSF support moves into core, and Web Components gain server-side docgen via Custom Elements Manifest with componentsManifest enabled by default for React, Vue 3, and Angular-Vite. CLI work focuses on AI skills installation (init/upgrade, framework-scoped instructions), opt-in browser opening, Windows path fixes, and dropping automigrations for upgrades to Storybook 10 or earlier. Numerous smaller fixes cover Angular-Vite, Next.js 16.3 Babel mode, Vite/Webpack5 Windows paths, and MCP tool-description limits.