The newest HackerNews stories, desensationalized.

Open model size and context demands are driving deployments from closed models toward large open models such as Kimi K3, but the 2.8T-parameter footprint and 1M-token context make memory capacity the central constraint. The post reports that AMD MI355X, with 288GB HBM and lower GPU-hour pricing, can outperform Nvidia B200 in aggregate throughput and beat B300 on performance per dollar despite lower single-stream speed and a slower prefill baseline in raw terms. The team measured about 952 tokens/s per node on a 1,024-in/400-out benchmark and 118 tok/s per stream, versus 498 tok/s and 90 tok/s on a TP16 B200 setup, while noting B300 still has higher aggregate throughput at higher cost. The benchmark setup includes a B200 limitation: two-node cross-node communication on decode dampens its efficiency, so MI355X’s single-node memory profile changes the tradeoff. Most gains came from targeted fixes in the ROCm stack rather than heavy custom kernel engineering: a missing top-k renorm symbol in sglang broke speculative decode and was patched with a small PyTorch function, raising single-stream and aggregate throughput significantly. Prefill performance was then optimized by aligning attention head counts to use AITER’s fast MLA path, turning a multi-minute bottleneck into a 2–3x improvement in time-to-first-token behavior. The post concludes that near-SOTA performance per dollar on AMD is now achievable with fewer framework patches than previous frontier ports. Readers point to the simplicity of some fixes as a reason to question whether benchmark correctness was preserved and whether those optimizations were fully verified.

Go 1.27 is a broad release that makes incremental but meaningful language, runtime, and library changes. It adds method-level type parameters so methods can define their own generics, and it improves generic function usage by allowing inference in conversions and function-typed containers. The compiler and runtime gain optimizations including size-specialized allocation, richer goroutine labels in tracebacks, stable goroutine leak profiling, and broader integer-divide and SIMD support for math, random, and map workflows. The standard library adds crypto/mldsa plus TLS and x509 integration, a new uuid package with RFC-9562-compliant generation and parsing, and a graduating encoding/json/v2 path that defaults to the new implementation while preserving compatibility. Testing ergonomics improve through testing/synctest.Sleep and an in-memory httptest.NewTestServer that auto-cleans and pairs with synthetic-time execution, while unicode support moves to version 17 and a number of small but breaking-behaving API changes land, including always-unbuffered timer channels and auto-draining HTTP response bodies. Toolchain and command changes include default stdversion vet checks, module-version-aware go doc, new fix analyzers, go mod tidy require-block normalization, localhost-only go tool trace behavior, and removal of bzr support. Release-note-adjacent changes include converting HTTP/2 to a real net/http internal package, adding HTTP/3 hooks, compiler optimizations like loop-invariant motion and switch table lowering, stricter linkname restrictions, optional map layout experiments, and a hardened os.Root path surface. The article presents the release as a practical, performance-oriented step forward for Go teams, especially those with heavy standard-library and concurrency usage.

Apple pursued server hardware in the late 1980s and early 1990s after the failed Macintosh Office effort, moving from the Big Mac concept into A/UX, AppleShare, and early 68040-era workstation-server hybrids. The company first shipped the Workgroup Servers 60, 80, and 95 as repackaged Quadras with A/UX support, then shifted to PowerPC and introduced the WGS 6150, 8150, and 9150 as the Green Giant line. The buildout became unstable in strategy, bouncing between promised A/UX 4, PowerOpen-A/UX plans, and a Novell NetWare port (Wormhole/Deep Space Nine), which alienated many Unix-focused customers and was ultimately abandoned. The article then documents the 9150 restoration in detail: capacitor recap, thermal repair, cracked ABS case mitigation, board reinforcement, and rebuilding of the chassis and drive subsystem despite brittle plastics and failing clips. It validates booting from Mac OS 8.6 via a ZuluSCSI setup, then partitions A/UX-style volumes for MkLinux pre-R2 installation, and records boot-time memory and hardware edge cases specific to MkLinux on NuBus Power Macs. The author explains why MkLinux mattered historically as Apple’s official Unix/Linux bridge and shows practical limitations versus direct Linux and later OS shifts, including the 601-only behavior, cache/memory tuning, and fragile multi-boot compatibility. Upgrades like Sonnet G3 cards and cache additions improved classic Mac OS responsiveness, but MkLinux compatibility broke with many video-card and CPU-path combinations, requiring conservative configuration. A resilient workaround kept the system usable through a mostly maximal, mostly stable dual-boot setup. A later reader note reinforces this was a meaningful community era: MkLinux on a Power Macintosh 8100/80 was a meaningful productivity jump but

Seedance 2.5 was launched as a ByteDance model focused on moving AI video creation from isolated clips toward end-to-end creative workflows. It keeps the prior multimodal architecture while extending one-pass generation to 30 seconds and adding multi-round continuation to make longer narratives easier, with improved transitions, shot coherence, and audiovisual alignment. The model expands multimodal input limits to 30 images, 10 video clips, and 10 audio clips per request, while also strengthening use of visual style, composition, motion, and clay-rendered spatial references for more precise scene control. It introduces more stable timestamp-based editing so creators can target segments for narrative, camera, and rhythm changes, and it extends green-screen, perspective, and reference-based editing workflows for production-like iteration. The rollout emphasizes practical use cases in education, industrial simulations, and robotics/autonomous-driving data generation, alongside explicit acknowledgment that physics realism and multi-subject interaction stability still need work. The update is positioned as both creative and commercial: available on Jimeng AI and Doubao Pro first, with API access via BytePlus ModelArk and a project page for onboarding. Overall, the positioning is that Seedance 2.5 improves controllability and realism enough to support longer-form stories and industry workflows, while explicitly treating remaining physical and temporal artifacts as open problems.

Diátaxis is presented as a lightweight framework for technical documentation that structures content around four user needs: tutorials, how-to guides, reference, and explanation. It argues that this structure improves what to write, how to write it, and how to organize documentation, while remaining implementation-neutral for documentation teams. The author describes adoption cases at organizations such as Vonage, Gatsby, and Cloudflare, where the framework helped make docs clearer for both readers and contributors during reorganization. The page emphasizes that documentation should follow these four forms consistently so each audience can quickly find what they need. Commenters generally agree that the model is practical for onboarding complex codebases and making authorship decisions explicit. They also report that it improves coherence by making tone and intent per page more predictable. Some contributors report better long-term maintainability when combining it with strict linking discipline and smaller reference files. The discussion also points out limits: over time, tutorials and reference pages can drift, and navigation can become slower when users must click through extra layers. Several comments therefore frame Diátaxis as a useful backbone rather than a universal solution, requiring careful governance and periodic review to stay effective.

The guide presents a practical, end-to-end tuning workflow for the ASRock BC-250 on Linux, arguing that its value comes from careful software-level optimization rather than hardware upgrades. It explains that the card starts at a locked 1500 MHz GPU clock and recommends replacing this baseline with cyan-skillfish-smu on CachyOS or Bazzite, then using its TOML configuration to narrow frequency ranges, adjust load targets, and lower per-state voltages in 50 mV steps after stability checks. It advises disabling performance mode during tuning, then enabling the service at boot only after stability is proven, with explicit caution about reboot safety. The guide then focuses on unlocking GPU compute units via BC250 Live Manager, from enabling all CUs to probing pairs if instability appears, because many boards can run with higher CU counts than shipped defaults. It adds a CPU overclocking/undervolting path with bc250-smu-oc, including installation differences between CachyOS and Bazzite, stress testing, and bc250-detect workflows that generate an overclock.conf profile. It closes with a shader-cache tweak (MESSA_SHADER_CACHE_MAX_SIZE=5G) to reduce recompilation overhead in heavier games and preserve performance. The tutorial repeatedly frames each change as reversible and test-driven, emphasizing incremental gains over risky defaults.

A thread begins with a Reddit-linked claim that Linux desktop share in North America has passed 10%, and immediately prompts scrutiny of measurement quality. Commenters note the cited figures look inconsistent, pointing to questionable breakdowns such as separating OS X from macOS and older conventions that can distort Linux and Chrome OS totals. Multiple responses argue that raw desktop/share numbers are increasingly blurred because many people now browse mostly on phones and tablets, while automated workflows and scraping can account for large shares of web traffic. One contributor cites Cloudflare telemetry suggesting only about 6% when filtering for likely humans, rising to 9.3% when bots are included, which supports the traffic-versus-user concern. Several participants suspect much of the apparent increase is from automated activity using Linux or macOS in datacenter and residential settings, especially as browser-driven data collection becomes more common. Another group disputes the result as implausible, saying 10% of desktops and laptops running Linux remains too high without a more precise definition. Others counter that Linux ecosystems are now polished and could genuinely be moving toward mainstream use, while a few add uncertainty over inclusion rules such as whether SteamOS is counted. By the end of the discussion, the thread frames the headline as a methodological warning rather than settled evidence of a major desktop shift.

Researchers from MIT Sloan and collaborators used simulations of 1,000 adults from age 22 to 89 to test GPT-5.2, GPT-5.6, and Gemini 3 Flash as financial advisors, finding that model guidance often improves saving, stock diversification, and age-adjusted risk choices compared with unaided decisions. Their benchmark model encoded long-horizon income, jobs, investments, taxes, and life events to define strong financial choices. The study showed that even unstructured user prompts often produced reasonable plans, while structured prompts with explicit assumptions and detailed personal context improved recommendations substantially. However, the models struggled with shocks like unemployment and showed limited active rebalancing, allowing portfolios to drift. Advice quality also varied across user traits; prompts linked to gender, financial literacy, and prior AI familiarity led to different asset allocations and saving rates, with estimated retirement-wealth gaps on the order of tens of thousands of dollars. Some variation reflected different prompt wording, while some appeared tied to model responses to demographic labels, raising fairness versus personalization questions. The authors argue that explicit standards for advice quality and demographic treatment are still missing, and that these models should be seen as accessible guidance tools rather than complete substitutes. They also note commercial impact, since model outputs mention products and providers more than users mention them, potentially shifting how people discover and compare offerings. Overall, the evidence favors AI as a strong baseline adviser for common lifecycle decisions, but not a robust replacement for nuanced, contextual, and emotionally grounded financial planning.

The Kyoto University team reports that habit formation is not merely repeated copy of behavior but involves a shift in neural control. In mice, they used a two-stage protocol: initial goal-directed training followed by four days optimized for habitual responding, allowing within-subject tracking of the transition. They identified a circuit from the anterior cingulate cortex to the retrosplenial cortex whose weakening connectivity predicts whether a behavior becomes habitual. A second pathway from the lateral orbitofrontal cortex to the central striatum controls how strongly the habit is expressed once formed, with stronger responses linked to higher execution. By manipulating these pathways, researchers could selectively increase the likelihood of habit formation or change execution levels, supporting distinct functional roles. The work also found durable individual differences in how intensely habits are performed after learning, challenging the idea that repetition produces a fixed behavioral template. The authors suggest this mechanism may explain why habits vary across individuals and could be relevant to both building beneficial routines and treating compulsive behavior disorders such as OCD.

The draft updates (D)TLS 1.2 guidance to remove weak key-exchange patterns that either lack forward secrecy or are hard to secure in practice, including non-ephemeral finite-field Diffie-Hellman, reusable ECDH public keys, and static RSA. It traces the risk model to concrete issues such as the Raccoon timing leak, subgroup and invalid-curve abuse, side-channel and fault attacks, and recurring RSA padding failures seen in Bleichenbacher-style exploitation chains like ROBOT and DROWN. The text also documents deployment friction in FFDHE negotiation: clients cannot reliably constrain groups because the server chooses them, custom groups are common, and fallback negotiation is weak, making insecure configurations more likely in mixed environments. Based on those risks, it sets strong requirements for TLS 1.2 only: clients must not offer and servers must not select non-ephemeral FFDH, DHE, or RSA suites, and static RSA key-exchange and fixed DH/ECDH certificate types are also effectively rejected. It keeps (D)TLS 1.0/1.1 out because they are already deprecated by RFC 8996 and excludes TLS 1.3 because it does not use these affected exchanges. The RFC then aligns registries by marking affected cipher suites and related client-certificate types as deprecated ('D') in IANA tables and by updating RFC 9325 recommendations so prior SHOULD-level guidance becomes MUST NOT where operationally necessary. The paper is positioned as a hardening pass for legacy compatibility boundaries rather than a redesign, emphasizing safer default negotiation and reduced complexity across deployments.

A soundness bug in Lean kernel #14576, reported in late July, was traced to nested-inductive parameter handling: phantom parameters could disappear from an auxiliary type during elimination, letting an ill-typed argument pass and yield a proof of False. The issue was exposed when a 2025-07-25 AI-assisted, sorry-free Collatz disproof by Ramana Kumar was reduced on July 28 by Kiran Gopinathan to a compact False proof, and a fix was shipped quickly in #14577, then reviewed and merged. The team emphasized the bug is in implementation, not meta-theory, and is reachable only through metaprogramming because the frontend still rejects the bad term. External checking did not fail because nanoda had a separate, earlier bug involving projection-node type-name validation, so independent verification worked only while both checkers had specific defects; a newer nanoda plus a fixed Lean kernel removes that path. The post argues removing metaprogramming is not a soundness solution, since untrusted elaboration is expected and adversaries could bypass it via alternate entry points like .olean artifacts. Ongoing verification work, including Mario Carneiro’s lean4lean project, had not yet covered inductive-type consistency, so it would not have caught this class of bug during the affected stage. The FRO added regression tests, strengthened kernel checks via multiple PRs, fixed additional metaprogramming-only mistakes from an AI-assisted cybersecurity audit, hardened core invariants, and made comparator.live run nanoda by default with daily tracking.

A guest post by Core-Lightning developer ddustin reconstructs the Coldcard entropy failure through commit history and call-chain analysis. It argues that two large firmware changes with near-empty commit messages—especially one labeled “runs” with a 5-character message across 1,534 changed lines and another with a 1-character message across about 1,000 lines—left critical security code poorly documented and hard to review. The “runs” change set added custom Micropython RNG symbols and turned off STM32 hardware RNG support with MICROPY_HW_ENABLE_RNG=0, likely to avoid a duplicate-symbol build error from redefining pyb_rng_get_obj. In that process, the firmware path using insecure fallback RNG code was unintentionally activated while custom overrides were not wired into the wallet entropy call. The post shows that wallet creation in v4.0.0 calls random.bytes(32) inside make_new_wallet, which maps to rng_get, and with the hardware path disabled this path used the weaker pyb_rng_yasmarang implementation. The analysis concludes that the resulting seeds could have low entropy and that the incident was driven by misunderstanding across C and Python layers, not cryptographic design intent. It is framed as a software process failure: overloaded commits, weak commit hygiene, and insufficient verification in a Bitcoin security product. The author uses this as a warning about embedded assumptions and urges stronger diligence, especially where money-loss risk is direct.

The post argues that modern graphing calculators can be turned into embedded Linux devices, focusing on the HP Prime G2 and the author's updated Linux port. It details the G2’s hardware—an i.MX 6Ultralite CPU, 256 MB RAM, and 512 MB storage—and contrasts this with older calculator models whose specs and locked-down ecosystems usually block Linux support. After finding an old unsupported port, the author rebuilt it to add working keyboard mapping, USB-serial connectivity, larger RAM-based images, an adapted Ilitek touchscreen driver, X11 startup, and a serial-safe module configuration, then shipped a prebuilt package and clear boot steps. The updated environment boots to a login prompt, runs X11 apps like xcalc and xeyes, launches Doom, and includes a C compiler for on-device development. The post includes hardware comparisons across TI, Numworks, and Casio, identifying Linux capability mainly on the HP Prime line and noting the TI Nspire CX II as the other notable Linux-capable option. It also discusses exam-mode style restrictions and vendor behavior, citing HP’s acknowledgement that system-level lockdown is possible even if not yet enforced. The article closes with G1-specific build instructions and a Windows 10 UEFI port mention, while warning that NAND flashing and hardware opening can brick the device. Commenters accept the novelty but question whether it materially improves routine calculation or coding workflows.

The article argues that technology progress unfolds across four distinct timescales and that confusion among them drives bad forecasts. Early research can require ten to twenty years before stable lab demonstrations emerge, and major concepts may take decades to become practical, as shown by the long progression from early artificial-neuron models to backpropagation-era deep learning and then to LLMs. The second phase is hype, where media attention, rebranding, and investor narratives can make a niche idea look universally adopted, as seen in swings from blockchain and the metaverse to AI agents and AI infrastructure. The third phase is deployment at scale, where software’s low marginal cost does not guarantee rapid adoption because organizations change slowly and operational integration is hard; Unix, Linux, and self-driving vehicles are offered as examples of long maturation and partial rollout. Hardware-heavy systems are usually slower still due to supply chains, regulation, and real-world reliability constraints. The final phase is economy-wide reshaping, which the author says typically requires many decades and can fail despite high expectations, citing dead-end commercial efforts such as Hyperloop commercialization. The argument is that research novelty and marketing momentum do not equal societal transformation. A key caution is that the harder engineering work of scaling, support, and resilience is where most timelines are lost. The comments add that AI feels faster today because of broad distribution and user experimentation, but the core distinction between hype and durable impact remains central.

A writer analyzes how to keep personal data for decades and outlines a practical architecture: four 8 TB drives arranged as two mirrored sets with periodic local-to-offsite synchronization, using Btrfs/ZFS-style checksumming to detect and self-heal latent corruption. The model separates two failure modes—whole-disk failure and latent sector errors—and treats annualized failure rates as the dominant uncertainty, using recent Backblaze-style drive statistics and sector-failure data. The analysis derives probabilities for a data-loss event over lifespan, accounting for rebuild time after a failure and the chance that remaining drives share the same bad sectors. Assuming approximate stationarity and independence between drives, the resulting estimates are intentionally conservative and often high. Results show one drive over 50 years is an unreliable design for unique data, while each added redundant mirror copy drops risk by orders of magnitude. The author estimates about 0.003 loss probability with two drives, about 8.7e-6 with three, and about 3.5e-8 with four, and approximates a factor-of-300 improvement per added parity redundancy up to four drives. They also discuss scrubbing cadence, checksumming value, and uncertainty around bitrot dynamics on modern disks. The post is self-critical about limited data and simplified assumptions, then includes a probability appendix to justify the method for readers who want to inspect the foundations rather than accept conclusions by authority. The practical recommendation is not immortality but layered redundancy and regular maintenance over theoretical guarantees.

Elena is presented as a lightweight library for building Progressive Web Components that emphasizes HTML and CSS rendering before JavaScript hydration, aiming to reduce heavy client-side behavior and improve baseline responsiveness. The API example defines a simple stack component with a custom element class, a single reactive property, and scoped styling, showing that component behavior can be built with explicit custom-elements primitives rather than framework-specific abstractions. Its creator positions it as a response to long-running issues in component systems, including accessibility gaps, server-side rendering friction, hydration-related layout shifts, flash of invisible content, and compatibility friction with analytics tooling and multiple frameworks. Elena targets teams that need reusable libraries and design systems across React, Next.js, Vue, Angular, or no framework, while preserving native web platform semantics. Claimed capabilities include a small 2.9 kB footprint, progressive enhancement, accessibility-first defaults, event and state batching, scoped style management without complex tooling, and optional SSR support. It avoids dependencies and lock-in by staying aligned with native custom elements and standard web APIs. Browser support is framed as tied to Custom Elements readiness, with the library itself tested in recent versions of major browsers. The docs close with practical onboarding paths such as quick start, examples, comparisons, FAQs, and a playground, and note three component models—composite, primitive, and declarative—that extend usage patterns. No criticisms or deployment trade-offs are documented in this content.

The post argues that Atom is technically superior to RSS and that RSS’s persistence is mostly due to ecosystem inertia rather than engineering merit. It concedes that many RSS shortcomings are cosmetic or manageable, including date formatting and broad XML interpretation differences, but insists the key practical issue is content semantics. Atom’s text construct model lets feed authors choose explicitly between plain text, entity-encoded HTML, or XML-encoded XHTML for titles, which prevents ambiguity and makes intended rendering testable across systems. In RSS, title encoding is not defined the same way, so producers cannot reliably represent strings containing markup-like characters such as tags without depending on reader-specific behavior. The author cites this as a realistic interoperability gap, not just a preference argument. A second major point is podcast history: early platform decisions by Apple around iTunes and podcast catalogs entrenched RSS support and constrained Atom adoption. The post notes that while Atom was designed to fix RSS limitations and became standardised, Apple’s handling of podcast tooling appears to have pushed RSS into de facto exclusivity. That legacy has left podcast readers and syndicators carrying compatibility hacks layered over years of RSS conventions. The article closes by acknowledging both the desire for broader migration and the practical drag from entrenched podcast infrastructure.

The article argues that Google’s RSS strategy repeatedly moved from endorsement to withdrawal, weakening confidence in the protocol. It recounts how Chrome once exposed native RSS discovery, then removed it without notice; how FeedBurner absorbed publishers’ feeds, added monetization and tracking, then cut APIs and core functionality; how Google Reader was closed in 2013 despite claims of falling usage that some users rejected; and how RSS delivery in Google Alerts, a Chrome extension, and Google News was dropped or deprecated before partial or temporary reversals. A 2021 promise to restore Chrome RSS is presented as further evidence of uncertainty. The piece frames this pattern as an embrace-extend-extinguish cycle in which dependency is encouraged and then withdrawn, leaving users to migrate or abandon RSS. Commenters generally echo concern that platform economics favor closed distribution, while also noting that RSS has not become mainstream for most users and lacks social engagement features that large readers now expect, making sustained adoption uneven.

A team building the Catalyst compiler for PennyLane described how their quantum-focused work led to an accidental JAX-to-MLIR-to-LLVM pipeline usable with pure Python and JAX NumPy code. They started from three needs: reuse mature classical infrastructure instead of rebuilding it, preserve higher-level control-flow structure for scalable hybrid algorithms, and keep quantum and classical differentiation working end-to-end. Because classical preprocessing, control, and postprocessing dominate quantum workflows, they wanted one executable for the full hybrid graph. They kept JAX as the Python front end for tracing and lowered captured graphs away from XLA into standard MLIR dialects, then to LLVM, with Enzyme handling backprop on classical code and custom passes handling quantum gradients. This design lets @qjit accept non-quantum functions and still compile, producing AOT binaries with no PJRT-like runtime dependency. They report benefits including native loops and conditionals without JAX control-flow primitives, dynamic shapes, easier custom dialect injection, and easier edge or bare-metal deployment, while not expecting speedups for standard large-scale deep learning where XLA is still highly optimized. The piece frames this as a pragmatic bridge from quantum tooling to broader compiler experimentation rather than a direct replacement for existing JAX/XLA workflows.

Morph says it is building inference infrastructure for its fastest open models across kernels, serving, routing, autoscaling, and capacity management. The company claims leadership-level optimization across these layers is the key to efficient frontier-scale deployment and is recruiting a performance engineer to improve speed, reliability, and cost profile. The role is positioned as hands-on work with the founders, with direct impact on production outcomes, and is tied to the company’s broader effort on specialized code-generation models. The team is described as small but supported by substantial compute, implying fast execution and tight engineering ownership. The job posting also highlights work on PD disaggregation research and technical work focused on kernel-level research plus custom speculative-decoding models.

The piece argues that AI pricing is almost uniformly token-based, with separate charges for input and output and output typically costing several times more. It lists headline rates for 12 models, showing wide variance from low-cost options such as DeepSeek V4 Flash 0731 at $0.09 per million input tokens and $0.18 per million output tokens to higher-priced models like Claude Fable 5 at $10 and $50. It notes context-window limits and flags that some vendor names in the snapshot omit a vendor label in the table, requiring interpretation for comparison. The core guidance is that naive cost calculators often misestimate because they ignore discounts, especially prompt caching and batch-mode reductions. Prompt caching can reduce repeated context costs by up to 90 percent, which is especially relevant for chatbot workloads that resend conversation histories. Batch processing can cut around 50 percent for workloads that can be delayed. The piece positions itself as an operational cost-planning resource rather than a model ranking, emphasizing monthly spend estimation for chatbots, agents, and API integrations.

The piece describes using an electric pressure cooker to make Vietnamese pho ga in under 30 minutes, mainly to solve practical constraints of cooking abroad at high altitude in Bogotá, where unfamiliar kitchens, ingredients, and lower atmospheric pressure make traditional simmering methods unreliable. It explains that elevating internal pressure raises water temperature above 100°C, shortening cook time and helping chicken become fall-off-the-bone tender in 15 to 20 minutes while also extracting body and gelatin for a rich broth. The author claims reduced boiling and agitation inside the pressure cooker keep broth clearer than stovetop methods, reducing the need for repeated skimming. The recipe uses a spice-heavy aromatic base of star anise, cinnamon, cloves, fennel, coriander, ginger, and onions, plus fish sauce and sugar, and can be adapted with local substitutions such as panela instead of rock sugar and instant pot-friendly workflow. The post emphasizes efficiency for working professionals and notes ingredient substitutions like rice vermicelli, available herbs, and chicken in Colombian markets. It frames the method as a practical response to travel constraints while preserving perceived “authentic” comfort qualities of pho. From the comments, readers reinforce the method’s long-term use and place the recipe among well-regarded pressure-cooker recipes, but several also challenge details like seasoning levels and depth of flavor versus slower cooking.

Nyctography is described as a substitution cipher system devised by Lewis Carroll in 1891 to let him capture ideas during nighttime without turning on a light, and the method was tied to the nyctograph device he also built. The system used a gridded card with sixteen quarter-inch square guide holes and symbols based on dots at corners and side strokes, with a large dot fixed in the upper-left of each character cell to mark alignment in low light. Carroll abandoned an earlier center-cut card design after poor legibility and replaced it with a square alphabet that he considered readable enough after long use. The writing scheme mapped the 26 letters and included extra symbols for words like “and” and “the,” mode-switch markers for figures and letters, and a marker for dates in DDMMYY format. It had no native capitalization, punctuation, or standalone digits, though later redesigns added those via dot-position variants. The six corner marks from the letter “d” encoded dates, while numerals were represented through letter patterns based on his Memoria Technica approach of consonant groupings for digits. The piece is presented as a historical note-making innovation, but it also notes that citation quality is limited and includes references to secondary sources for verification.

The article shows how QSYRUPWD, intended to return encrypted user password data for administration workflows on IBM i, does not directly align with John the Ripper formats at QPWDLVL 24, despite legacy compatibility. The author built a CL tool and traced calls with STRTRC, then used System Service Tools disassembly to confirm that key crypto work happens via SCV 10 calls into #crcipher routines. They identified multiple CIPHER operations and reproduced their behavior: first SHA-1, MD5, then nonstandard Rijndael-like CBC/AES-256 usage with 32-byte blocks, and later AES-ECB key-derivation steps using reordered slices of a SHA-1 digest. The writeup maps stack/register state to specific input buffers, proving the operations are standard in their primitives but chained with undocumented formats and internal struct construction. It derives how legacy DES bytes and EBCDIC usernames are normalized and merged into a deterministic 32-byte key for final NT hash recovery, with the DES-derived part zeroed when unavailable. By parsing the returned 2000-byte layout and reversing the dependency order, the method extracts DES, SHA-1, and NTLM material directly from the API response without altering credentials or user interaction. The same chain was validated against scripts and John the Ripper formats (as400-des, as400-ssha1, NT), and the approach also worked when QPWDLVL was 4, where legacy fields were empty but NT recovery still succeeded via embedded ZLS output. The benchmarks quantify practical impact by comparing NTLM with PBKDF2 cost, showing why recovering NTLM-equivalent verifier material is critical for offline cracking economics. The article concludes that ALLOBJ + SECADM* is effectively access to reusable credential artifacts and recommends tight authority control, abuse-

NetBSD announced the 11.0 release and provided direct guidance for installation media by architecture, recommending preferred images from its CDN, U-Boot ARM downloads, DVD images over smaller CD images when possible, and .img images for flash media after decompression. The project explicitly noted that this release was delayed but published with several unresolved security issues rather than waiting for every fix, citing a large and ongoing report rate linked to AI-related tooling. It said it avoided untested changes by shipping release candidates, automated much of the process, and identified manual signing as one remaining bottleneck. Remaining open work was listed as pullup requests for hdaudio access checks, ipfilter null-pointer safety, and PF fragment reassembly use-after-free behavior. The same items were not in default kernels for ipfilter and PF at release time, and will be applied to the stable branch after 11.0. NetBSD stated these fixes will ship in 11.1, with a target window of roughly two months. The article frames the release as a balanced tradeoff between timely delivery and transparent reporting of known risk.

The post introduces Explorative Modeling as a training objective that can trade a single-step prediction for K sampled predictions and backpropagate only the best match, shifting the loss minimizer from an average toward real data modes. This is framed as a solution to the multi-answer problem that causes blurred outputs when models are forced to predict one point in multimodal distributions, and as a complement to, not replacement for, existing factorization methods such as autoregression and diffusion. The author argues that scaling has overemphasized data and parameters while ignoring generative expressivity as a third pretraining axis, proposing that exploration effectively increases this capacity and can be increased over training in a Chinchilla-like compute-optimal way. In image experiments, adding exploration to existing pipelines preserves architecture and hyperparameters while reducing required data by 6.2x, improving FLOP efficiency by 4.1x, and reaching state-of-the-art FID levels on ImageNet 256 with substantial convergence speedups across related recipes. Similar monotonic improvements were reported in video and masked language domains, with gains rising with scale (roughly 7% to 36% from more data, and 13% to 23% from larger models) and no sign of plateau at tested K. The strongest claim is that exploration can also support end-to-end generation, replacing inference-time factorization steps with training-time search: behavior cloning ran at one forward pass versus 100, and world model evaluation improved score with far fewer passes. The post also reports better generalization from larger K, arguing that matching each prediction to the nearest target avoids memorizing inconsistent noise. It includes caveats that the method helps most when each predictionЧ

Glyphs 4 is presented as a major iteration for font design, combining pen-point based variable strokes, COLRv1 color workflows, visual kerning groups, a unified edit/preview interface, and a stronger sidebar-driven tool layout. The update highlights shape- and stroke-level controls such as star nodes, cap and corner options, batch handle edits, and a Slanter filter for oblique transformations, with better interpolation and master browsing for large families. It adds in-app testing via live text preview for alignment, tracking, line spacing, and OpenType features without switching contexts. Designers are also given broader production tools for reuse and scaling, including components, reusable shapes, batch glyph edits, and a richer export matrix spanning OpenType (CFF, CFF2, TTF, WOFF, WOFF2), variable fonts, images (PNG, SVG, PDF), and symbol/image formats. Extendability is emphasized through hundreds of plug-ins, a plugin manager, inline OpenType syntax checking, and a Python 3 API with scripting tutorials. The page closes by framing the product as aimed at full control of curves, color/font styling, and production-ready deliverables, with extensive support materials to onboard users.

The update is a technical progress report on porting an MMU-capable Linux on RISC-V to the ESP32-S31 module (ESP32-S31-WROOM-3 E1H16R16V), with explicit feature tables showing most subsystems as untested, several as in-progress, and some marked not implemented or not planned. Core claims focus on practical integration: a custom CLIC path was introduced because S31 appears to route interrupts through CLIC/CLINT behavior unlike Linux’s usual expectations, and OpenSBI was modified to use XIP from flash and internal SRAM to fit the 16 MB PSRAM limit. The post notes that standard RISC-V CSR expectations were unreliable on S31, so behavior had to be inferred from other documentation and probing. It also documents that kernel timekeeping and privilege assumptions were unusual, with SCLIC use being nonstandard and interrupt wiring diverging from common ESP-IDF usage. A Linux 6.12 build was selected because mainline later removed RISC-V XIP, so earlier support was needed. Runtime checks include successful CoreMark throughput, CPU identity reporting, memory size, process list, and basic /proc outputs, indicating a booted system that can execute commands and report system state. The release is framed as incomplete because no stable-green feature areas were listed and many drivers are still unproven, so the work reads as a constrained prototype with meaningful low-level evidence but limited platform readiness.