HackerNews Digest

The page is titled “comma.ai – make driving chill.” It displays a series of images identified only by their alt‑text descriptors. The first image shows the “comma four device,” presumably the company’s hardware product. Subsequent images present logos of several automobile manufacturers—Hyundai, Kia, Lexus, and Toyota—suggesting relevance to those brands. Additional logos belong to media and technology outlets: Linus Tech Tips, Snazzy Labs, The Verge, Car and Driver, Consumer Reports, and Road Show, indicating coverage or endorsement. No further narrative, product specifications, or functional details are provided in the scraped text beyond these visual references.

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The provided content consists only of the title “Y Combinator” without any additional text or information.

- Uses C23 for new projects, targeting GCC/Clang/MSVC; asserts `CHAR_BIT == 8` at compile time. - Defines concise fixed‑width aliases (`u8`, `i16`, `f32`, `usize`, etc.) mirroring Rust style. - Implements a length‑prefixed string type: `typedef struct { u8 *data; isize len; } String;` with helpers for C‑string and buffer construction. - Follows “parse, don’t validate”: creates opaque structs via private headers so only trusted constructors can produce valid values, improving compile‑time safety. - Provides a tuple macro (`Tuple2(T1,T2)`) leveraging C23’s compatible tagged types, though pointer members require work‑arounds. - Emulates sum types with an `enum` error code and a discriminated union (`MaybeBuffer`) containing a success flag, value pointer, or error, encouraging explicit result checking. - Minimal dynamic‑allocation usage; prefers arenas when needed, otherwise switches to higher‑level languages. - Avoids most `string.h` functions, re‑implements OS‑specific helpers, and considers slice‑like abstractions for safer memory access.

Gas Town is Steve Yegge’s experimental, fully‑vibecoded agent orchestrator that runs dozens of coding agents in a metaphorical “town.” The system was built in 17 days (≈75 k LOC, 2 k commits) and serves as a design‑fiction prototype rather than a production tool. Its core insights include: - **Design as bottleneck:** With agents generating code rapidly, human effort shifts to architectural planning, feature prioritization, and specification, tasks agents cannot automate. - **Hierarchical role‑based agents:** A “Mayor” interfaces with users, dispatches tasks to temporary “Polecats,” supervised by “Witnesses,” while a “Refinery” resolves merge conflicts and can re‑imagine work when branches diverge. - **Persistent task tracking:** Agent identities and work units (“Beads”) are stored as JSON in Git, enabling disposable sessions and “seancing” to query prior state. - **Continuous work queues:** The Mayor feeds atomic tasks to agents; supervisors periodically nudge idle workers, a provisional solution to current models’ lack of autonomous task polling. - **Merge‑conflict handling & stacked diffs:** The Refinery processes PRs; stacked diffs are suggested to reduce conflict complexity for high‑throughput agent output. - **Cost considerations:** Current monthly spend is estimated at $2‑5 k, inflated by inefficiencies; future, more efficient orchestrators could be cost‑effective versus senior developer salaries. - **Code‑visibility debate:** Gas Town embodies “vibecoding” (no human code review), prompting discussion on acceptable distance between developers and generated code based on domain, risk, project type, and team maturity. The project highlights emerging patterns for scalable agent orchestration while exposing critical design, cost, and governance challenges.

The author added Unicode‑property support to the Go‑based pattern matcher Quamina. Standard Go libraries lag at Unicode 15.0 (Sept 2023) while Unicode 17.0 (Sept 2024) is current, so the author downloaded UnicodeData.txt, extracted the first and third fields, and built ranges of code‑point pairs for each of the 37 Unicode categories and their complements, yielding 14 811 ranges and ~5 122 lines of generated Go code. Pre‑generating full automata produced 775 k lines (≈12 MiB), causing long startup times and IDE crashes. The solution switched to lazy caching: an automaton is constructed on first use and stored, raising the rate of adding Unicode‑property regexes from ≈135 /s to ≈4 330 /s. The resulting automata are wide but shallow because they operate on UTF‑8 characters (max 4 bytes), allowing matching at hundreds of thousands to millions of messages per second. The author notes that routine tasks (fetching/parsing UnicodeData.txt, code generation, tests) could have been done faster with a large language model like Claude, but tooling and skepticism prevented its use. Upcoming work includes numeric quantifier syntax (e.g., a{2‑5}) to complete regex support and a planned Quamina 2.0 release.

The supplied text consists solely of the heading “Title: Proof of Corn” and contains no additional material. Consequently, there is no substantive content to summarize.

Microsoft supplied FBI agents with BitLocker recovery keys for three laptops seized in a fraud investigation tied to Guam’s Pandemic Unemployment Assistance program. BitLocker, Windows’ default full‑disk encryption, automatically uploads recovery keys to Microsoft’s cloud, allowing the company—and, by legal process, law‑enforcement—to retrieve them. Microsoft has indicated it receives roughly 20 such government requests annually. The FBI obtained a warrant six months after seizing the devices, after which Microsoft provided the keys. Security experts, including Johns Hopkins cryptographer Matthew Green, highlighted privacy risks: a breach of Microsoft’s cloud could expose recovery keys, though attackers would still need physical access to the drives. Microsoft did not comment further on the specific request.

Noora Health India Private Limited, a partner of Noora Health, develops content, technology platforms, and products to train family caregivers in India, Bangladesh, Indonesia, and Nepal. Since 2014, it has educated over 43 million caregivers across 12,800+ facilities via its Care Companion Program, achieving reductions in post‑surgical cardiac complications (71 %), maternal complications (12 %), newborn complications (16 %) and newborn mortality (18 %). The company, a Skoll Foundation awardee and Audacious Project grantee, seeks an AI/ML Engineer to design, build, and maintain end‑to‑end AI systems: data curation, preprocessing, prototyping, deployment, and production monitoring. Responsibilities include defining model success metrics, integrating AI features into existing infrastructure, guiding best practices, documenting research, and collaborating with engineers, product managers, designers, and clinical experts. Candidates must have ≥4 years of AI/ML experience, a BS/MS in a relevant field, strong Python and machine‑learning fundamentals, a shipped deep‑learning project (CV, NLP, or RL), and cloud/database proficiency (GCP/AWS/Azure, SQL/NoSQL). The role emphasizes diversity, equity, inclusion, and clear communication. Applications are submitted via the provided link.

On 22 Jan 2026 an automated routing‑policy change on a single Miami edge router unintentionally advertised internal IPv6 prefixes to external peers and providers, creating a BGP route leak (RFC 7908 Types 3 & 4). The mis‑removal of the “6‑BOG04‑SITE‑LOCAL” prefix lists made the policy accept all “internal” routes, causing AS 13335 to export prefixes received from peer AS 32934 to upstream provider AS 3356 and other peers. The leak lasted ~25 minutes (20:25 – 20:50 UTC), producing congestion on the Miami‑Atlanta backbone, up to 12 Gbps of ingress traffic discarded by firewall filters, elevated loss and latency for Cloudflare customers and external networks. Key timeline: - 19:52 UTC: buggy change merged. - 20:25 UTC: automation applied. - 20:40 UTC: unintended advertisements detected. - 20:50 UTC: manual revert and automation pause. - 21:47 UTC: code revert. - 22:07 UTC: automation health confirmed. Remediation actions include patching the automation bug, adding BGP‑community reject rules, integrating policy‑evaluation into CI/CD, improving configuration‑change detection, validating vendor support for RFC 9234 (Only‑to‑Customer attribute), and promoting RPKI ASPA adoption.