Capital One to acquire Brex for $5.15B
Community Discussion
Comments show a mixed assessment of the Capital One‑Brex acquisition. Many note the steep drop from a $12 billion peak to a $5.15 billion sale, viewing it as favorable for early investors but harsh for late‑stage backers and employees whose equity is now underwater. Opinions diverge on the strategic outcome: some cite the deal as a realistic valuation reset and a boost from Brex’s growing revenue, while others criticize the failed AI pivot, competitive pressure from rivals, and Capital One’s credit‑reporting practices. Overall sentiment balances cautious optimism about the asset’s growth with concern over stakeholder impacts.
GPTZero finds 100 new hallucinations in NeurIPS 2025 accepted papers
Summary
GPTZero’s analysis of the NeurIPS 2025 accepted papers identified 100 instances of fabricated or inaccurate scholarly information. For each flagged entry, the tool compared the cited title, authors, venue, DOI, arXiv identifier, and publication year against existing records. The majority of “hallucinations” involved completely nonexistent papers, mismatched author lists, incorrect journal or conference details, and placeholder arXiv IDs (e.g., “arXiv:2401.XXXX”). Some citations partially matched real works—sharing a keyword or a single author—but differed in title, venue, or year, rendering them unreliable. In several cases, legitimate author names were paired with fabricated titles or vice‑versa, and DOI strings pointed to unrelated articles. The audit also uncovered repeated patterns of fabricated references across diverse domains (e.g., robotics simulators, uncertainty quantification, vision‑language adaptation, ocean nutrient modeling). Overall, GPTZero demonstrated that a substantial portion of the bibliographic metadata in the examined NeurIPS submissions could not be verified, highlighting pervasive citation hallucination.
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Community Discussion
The comments express strong concern that AI‑generated hallucinated citations are slipping through peer review, undermining scientific credibility and amplifying existing incentives to prioritize publication volume over rigor. Repeated themes include criticism of insufficient verification tools, calls for automated citation checks, and demands for harsher penalties for undisclosed AI use. Some participants note the potential for the issue to highlight reproducibility problems and suggest reforms to review workloads and incentive structures, but overall the sentiment is wary and calls for stronger safeguards.
Show HN: isometric.nyc – giant isometric pixel art map of NYC
Summary
The provided material contains only the title “Isometric NYC” and no additional text to summarize.
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Community Discussion
The comments overwhelmingly praise the isometric NYC map for its visual appeal, technical ingenuity, and detailed write‑up, with many users expressing excitement, personal connection to the city, and interest in extending the approach to other locations or styles. Frequent discussion points include the underlying models, fine‑tuning process, cost considerations, and technical hurdles such as CORS limits. A notable minority critique the result’s resemblance to pixel art, question the artistic value of AI‑generated imagery, and raise broader concerns about scale, impact, and authenticity of AI‑driven creative work.
Why does SSH send 100 packets per keystroke?
Summary
SSH clients (e.g., macOS stock ssh) now add keystroke‑timing obfuscation: after each keypress they emit a burst of “chaff” packets (~20 ms apart) to mask typing speed. These packets are SSH2_MSG_PING messages sent when the server advertises the [email protected] extension. In a high‑performance game run over SSH, a single keystroke generated ~90 packets per second, 66 % of which were 36‑byte data packets, inflating CPU usage and bandwidth by ~50 %. Disabling the feature client‑side with ObscureKeystrokeTiming=no or server‑side by removing the [email protected] advertisement (by patching Go’s crypto/ssh library) stopped the chaff, cutting total CPU from 29.9 % to 11.6 % and halving bandwidth (≈6.5 Mbit/s → 3 Mbit/s). The investigation used tcpdump/tshark scripts, identified the packet pattern, and confirmed the cause via ssh ‑vvv logs. The author notes the usefulness of LLM assistance in debugging but stresses the need to maintain a patched library.
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Community Discussion
Comments show broad interest in the recent SSH keystroke‑timing obfuscation patch, with many acknowledging its security purpose but questioning its performance impact for latency‑sensitive use cases such as games. Readers suggest upstreaming configurable options, using alternatives like telnet, or tuning TCP parameters to reduce packet overhead, while warning against disabling the feature in production. The write‑up is praised for clarity, yet some participants criticize reliance on language models for debugging and note occasional off‑topic frustrations about network latency and infrastructure decisions.
I was banned from Claude for scaffolding a Claude.md file?
Community Discussion
Comments express strong frustration with Anthropic’s support and account‑banning practices, describing unresponsive help channels, opaque content‑filtering blocks, and perceived arbitrary violations of terms of service. Users note occasional appreciation for Claude’s capabilities but criticize the lack of clear explanations, inconsistent enforcement, and difficulty appealing decisions. Many discuss seeking alternatives such as other providers or self‑hosted, open‑source models to avoid vendor lock‑in, while also highlighting concerns about safety filters, token costs, and the need for transparent, reliable customer service.
Improving the usability of C libraries in Swift
Summary
The article explains how to make C libraries feel native in Swift without modifying the original C code. By creating a module.map and using swift‑synthesize‑interface you can view the automatically generated Swift API, which initially mirrors C (global functions, prefixed names, unsafe pointers, and opaque‑pointer types). Swift‑specific annotations—applied either directly in the header or via a separate .apinotes YAML file—transform these mappings:
* enum_extensibility marks C enums as closed, producing Swift enums with cases and raw values.
* SWIFT_SHARED_REFERENCE converts reference‑counted opaque pointers into Swift classes that manage retain/release automatically.
* SWIFT_RETURNS_RETAINED indicates functions that return ownership, enabling Swift to balance releases.
* SWIFT_NAME adds argument labels and can re‑export functions as methods on the appropriate Swift class.
Using the WebGPU header as an example, the process yields Swift‑style constructs such as WGPUInstance(descriptor:), instance.createSurface(descriptor:), and queue.writeBuffer(buffer:bufferOffset:data:size:), with automatic memory management and clearer syntax. The approach relies on Swift 6.2.3+ bug fixes and works for any well‑designed C library.
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Community Discussion
The feedback is overwhelmingly positive, highlighting appreciation for the presented material. The reader finds the previously used naive method cumbersome and values the introduction of newer language features that were previously unknown. There is a clear intention to incorporate the demonstrated techniques in future C binding projects, indicating confidence that the approach will improve code quality and reduce clunkiness. Overall sentiment reflects enthusiasm for adopting the discussed strategies.
Bugs Apple Loves
Summary
The page “Bugs Apple Loves” presents a quantitative model for estimating the productivity loss caused by unresolved Apple software bugs. It defines four components:
- **Base Impact** = Users Affected × Frequency × Time per Incident, measuring the direct time each affected user spends dealing with a bug.
- **Power User Tax** = Σ (Workaround Time × Participation Rate), capturing additional effort from users who create or apply temporary fixes.
- **Shame Multiplier** = Years Unfixed × Pressure Factor, scaling impact by the duration the bug has remained unresolved and its perceived urgency.
- **Verdict** = Human Hours Wasted ÷ Engineering Hours to Fix, indicating how many times the wasted human labor exceeds the engineering effort required for a fix.
The site invites readers to edit the underlying numbers, emphasizing that each bug’s parameters differ but the overall calculation remains real‑valued.
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Community Discussion
The comments collectively view the design as evidently AI‑generated, expressing a perception that the style lacks originality and appears formulaic. This viewpoint suggests disappointment or criticism regarding authenticity and creative effort, implying that the aesthetic is recognized as generic or automatically produced rather than handcrafted. Overall, the feedback reflects skepticism toward automated design tools and a preference for more distinctive, human‑crafted visual solutions.
Turso is an in-process SQL database, compatible with SQLite
Summary
The repository “tursodatabase/turso” on GitHub provides Turso, an in‑process SQL database that is compatible with SQLite. The page displays a notice stating “You can’t perform that action at this time,” indicating a temporary access restriction. Visual assets referenced include: Turso Database logo, a crate icon (likely for Rust packaging), NPM badge (JavaScript/Node.js), PyPI badge (Python), Maven Central badge (Java), and two Discord community links—one for chatting with core developers and another for broader user discussions. No additional technical documentation, code examples, or usage instructions are present in the provided excerpt.
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Community Discussion
The comments express mixed reactions, noting that the project still lacks key SQLite features such as savepoints, window functions, and database attachment, which raises doubts about its readiness. Some view a focus on unique capabilities—like supporting multiple writers or targeting OLAP scenarios—as more marketable than simply branding it as a SQLite successor. Praise is directed toward SQLite’s long‑standing impact and its creator, while interest is shown in the Rust‑based rewrite and its active development. Skepticism remains about its necessity and language‑specific suitability.
Why medieval city-builder video games are historically inaccurate (2020)
Summary
The post critiques medieval city‑builder video games for deviating from historical settlement patterns. It notes that early titles such as *Sim City*, *Caesar*, and *Age of Empires* gave rise to medieval‑themed games like *Settlers* (1993) and *Knights and Merchants* (1998), which typically start with a central village and expand linearly through resource gathering and production chains. Historical research shows medieval villages were often stable in size, subject to subsistence limits, tithes, taxes, disease, warfare, and environmental risks, with growth occurring only under exceptional conditions. Archaeological evidence from West‑Brabant identifies three planned settlement types—circular manor (BORCH), linear street settlement, and hybrid exploitation village—each laid out by land surveys, water access, and defined field strips. Management involved crop rotation (three‑field system), pasture, road maintenance, and regular tax collection. The author suggests more accurate games would allow pre‑planned layouts, curved roads, flood mechanics, and explicit tithe/land‑owner interactions, but acknowledges gameplay, path‑finding, and market expectations often favor simplified, linear growth models.
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Community Discussion
The comments highlight a prevailing tension between historical fidelity and gameplay enjoyment, with many players prioritizing immersive, satisfying mechanics over accurate medieval layouts or economic details. Several titles such as Banished, Manor Lords, and older Settlers games are cited as examples that balance realism and fun, while older RTS titles are noted for simplifying or omitting complex historical factors. Nostalgia for medieval builders is acknowledged, yet there is consensus that realistic constraints often hinder fun, leading designers to deliberately abstract or streamline historical elements.
Qwen3-TTS family is now open sourced: Voice design, clone, and generation
Community Discussion
The comments express overall enthusiasm for the new TTS model’s quality, noting it often surpasses previous systems yet sometimes produces erratic or overly emotive output. Users discuss practical challenges such as running the model locally on non‑CUDA hardware, especially macOS, and seek clearer installation guidance. There is strong interest in applying the voice‑cloning feature to audiobooks, game dialogue, and restoration of old recordings, while comparing its performance to existing services like ElevenLabs and Parakeet. A minority voice concerns about the company’s leadership and political stance, but the dominant sentiment remains curiosity and optimism about experimenting with the technology.