The newest HackerNews stories, desensationalized.

Researchers at Drexel University report that a nonelastic hydrocarbon blend can crack when stretched, contradicting the long-standing view that simple fluids only flow. In repeated extensional rheology tests, the fluid produced loud pops and split catastrophically, a behavior the team described as brittle fracture. Work on complex fluids had previously tied liquid fracture to elasticity, especially in viscoelastic systems like polymer melts, but this study shows a nonelastic system can fail the same way. The team points to 1990s theory from Daniel D. Joseph and updated measurements suggesting cavitation can nucleate vapor-filled bubbles that then trigger fracture when pulled hard enough. Once a crack starts in simple fluids, propagation is much faster than in complex ones, measured around 500 to 1,500 meters per second versus about 0.07 meters per second in melted polystyrene. Both fluid classes fractured near a similar critical stress of roughly 2 megapascals, with failure appearing proportional to viscosity times strain rate. The experimenters note that their instrument’s speed limit may have prevented observation of even less viscous fluids such as honey or water. They propose transparent fluids, rapid freeze-capture, and nanoscale microscopy to watch crack nucleation and growth more closely. The researchers also see possible relevance for fiber spinning, inkjet printing, protective systems, and soft robotics.

The article introduces Mesh LLM as a peer-operated alternative to proprietary hosted LLM APIs, arguing that teams can keep control of model behavior, hardware, and data paths while reducing dependence on single-provider billing. It describes a mesh of iroh endpoints that can execute requests locally, forward them to a peer with the model loaded, or split a model across machines through a pipeline stage approach called Skippy. The system exposes a single OpenAI-style API at localhost:9337/v1 so existing clients can continue to connect without major changes. Internally, it uses pluggable plugins, model-capability manifests, and MCP/HTTP/inference interfaces, with a catalog covering 40+ models from small laptops to a 235B MoE model. For large models, layers are partitioned into consecutive ranges across nodes and activations stream between stages, preserving normal client semantics while distributing compute. Networking relies on iroh identity keys, QUIC transport, NAT traversal, and relay fallback with no central coordinator, while ALPN labels and stream-byte routing separate gossip, inference tunneling, route queries, peer lifecycle, and plugin traffic. Mesh control plane logic layers trust and admission checks on top of iroh’s authenticated links, and operators can run public or private meshes from a small client footprint around 18 MB, with a mobile ACP-focused client roadmap. Commentary in the piece indicates adoption questions are likely to focus less on API compatibility and more on real-world throughput, latency, security, and competitive parity with existing distributed inference tooling.

Goeteia is presented as a browser-resident Scheme system compiled to WebAssembly that ships its own ~70 KB gzipped compiler payload and recompiles source on each run in about 15 ms without any server-side compilation. The project is self-hosting: the compiler is written in the Scheme subset it compiles, and the CI process checks a byte-identical fixpoint and runs tests through both compiler stages. Its runtime design uses i31ref for unboxed fixnums, Wasm GC structs for pairs and records, and ref.eq for eq?, with hygienic compile-time macros via syntax-rules and syntax-case features such as nested ellipses and datum->syntax. Function calls use typed function references with per-arity and generic closure entries so variadic calls and apply stay inexpensive, and tail calls map to return_call, enabling a 100 million iteration loop in constant stack within ~150 ms. Continuations use Wasm exceptions with O(1) capture and inner-to-outer unwinding through winders. The web stack includes web sx signals, web html reactive rendering, direct web js FFI, web three for Three.js-style scene building, and web gl/web glsl for low-overhead frame command streaming with shader s-expressions. The backend mode with Igropyr keeps RPC and payloads in s-expressions, preserving exact numeric forms like rational results, while web fetch/ws/sse/json cover host integration. Developers are told build and test commands support full verification, and execution targets include Node 22+, modern Chrome/Firefox/Safari with Wasm GC and tail calls, and wasmtime, with Chez Scheme required only for bootstrap from source.

The post presents RISCBoy as a complete open-source, scratch-built portable console architecture, including a RV32IMC-compatible CPU, raster graphics pipeline, bus fabric, memory controllers, UART, GPIO, and KiCad PCB files, effectively creating a modern reimplementation with a handheld lineage. It is designed to run on constrained FPGAs such as Lattice iCE40-HX8k using synthesisable Verilog 2005, while retaining a fallback RV32I configuration for smaller devices like UP5k. The repository is organized into HDL, tests, documentation, and board resources and uses submodules, with a recursive clone required for full processor test coverage. Setup guidance covers both hardware and software validation: custom RV32 toolchain generation, environment sourcing, simulation with Xilinx ISIM, and test commands for HDL suites, with notes about required paths and special git flags. A full open-source build flow is documented for gateware synthesis through Yosys, nextpnr, and Icestorm, plus experimental ECP5 support with memory-system changes for the LEF5UM5G-85F-EVN board. The project also includes practical hardware notes such as Rev A PCB compatibility with iTead prototyping and a future Rev B planned after firmware and gateware maturity. The developer points to an extensive directory breakdown spanning bus fabrics, graphics, processor core, peripherals, documentation, scripts, and test collections, signaling reproducible long-term development. Discussion around the post emphasizes that the design is technically dense and self-contained, but also approachable for builders willing to configure their own toolchain and workflow.

The post is a practical technical guide on drawing in 9front using paint(1), aimed at users comfortable with Plan 9 workflows and low-level tools. It starts by positioning 9front as an older but capable setup and recommends touch-capable 2-in-1 devices, while noting mouse, finger, tablet, keyboard-based input, and drawterm/VNC as alternatives for different hardware constraints. It then treats paint(1) as an intentionally direct drawing environment with major limitations: no pressure sensitivity, no layers, and no cut-and-paste, so users rely on repeated saves, reloading, manual undo behavior, pan/zoom, and brush/eraser operations. The tutorial details canvas semantics and color handling, including how canvas bounds expand from drawn or erased pixels, how background colors are changed safely, and how exact editing is done with commands such as w, r, |, <, and > for transformation and external image processing. Later sections provide a full image-processing stack with crop, vcrop, resize, and rotate, including precise coordinate-based cropping math when drawings spill beyond intended dimensions and practical methods for fixed-size output using crop marks or margins. It also introduces pico for color-based layer-like workflows, tracing source images, and advanced replacements, then shifts to export and publication by converting to PNG, converting back to image(6) when needed, and sharing via phone photos, mounted drives, sshfs, drawterm, snac, and webserver paths. The post closes with references and a short note, "I don't even use plan9," signaling a self-aware niche, archival tone.

The paper analyzes how much energy cells must spend to compute information about external ligand concentrations using a two-component signaling model based on Berg-Purcell sensing principles. Grounded in Landauer’s principle, it argues that cellular computation is inherently dissipative and cannot be performed without breaking detailed balance. The authors calculate an explicit energy budget for a noisy sensing network and show that higher information acquisition demands higher dissipation. They suggest this energetic burden is not a minor detail but a design constraint, especially for cells in low-resource settings such as bacterial spore germination systems.

A user described years of intermittent failures to wake a laptop through Thunderbolt docks, including a long period where the earlier CalDigit TS3 and later TS4 often left systems unresponsive on wake. They had expected Thunderbolt 4’s conformance requirement for wake-from-sleep to solve this, but even with a MacBook Pro M1 Max and ThinkPad X1 Carbon and a newer dock, reliability stayed inconsistent. After replacing an older BenQ monitor with an ASUS ROG Swift PG27UCDM in 2025, wake became consistently quick, usually under a second, and they had no further failures in that configuration. The writer does not claim a known fix and suggests firmware or hardware differences could be involved, then shared their exact setup to help others compare. In the comments, readers repeatedly describe uneven experiences across devices, with some seeing similar instability while others report stable docks, suggesting outcomes depend on host firmware, monitor firmware, cable quality, and implementation details. Several commenters propose that non-Apple Thunderbolt behavior varies more than users expect and that the monitor swap may be useful as a practical troubleshooting clue for a common productivity issue.

The analysis says hyperscaler-backed neocloud operators CoreWeave and Nebius have scaled sales, backlog, and contracts quickly by offering fast deployment of Nvidia GPUs, high-density facilities, and software tools that improve model FLOPs utilization versus baseline metrics. It notes large long-term commitments from Microsoft and Meta that are an order of magnitude above both firms’ current annual revenues, with neocloud agreements treated as operating expenses for buyers rather than capital expenditures on hyperscaler balance sheets. The writer argues this capex shift improves near-term financial optics for AI platforms while pushing enormous buildout spending onto neocloud balance sheets, then details how Nvidia’s equity investments and GPU supply agreements create a circular financing loop because neoclouds buy large volumes of Nvidia chips and receive resale commitments and balance-sheet support. Debt is the main funding engine: CoreWeave has reported rapid capex growth, shrinking cash, and rising debt despite positive operating cash flow, while Nebius, though less leveraged, also faces a multi-billion-dollar funding gap to meet active-power expansion targets. Both firms use GPU-backed debt structures, including delayed draw tranches with investment-grade pricing tied to large customer contracts, but higher rates in 2026 and high interest burdens already consume a significant share of revenue and EBITDA. The piece also flags a key commercial risk: utilization, pricing, and obsolescence as hardware generations turn faster, while remaining questions persist about whether this capital intensity can ever converge to durable positive free cash flow. Commenters mirror these tensions, debating whether Nvidia’s 2 billion share is meaningful relative to scale and arguing ne

The author recounts an AI course at the University of Guelph where Lisp was taught as the language of symbolic AI, then contrasts that history with today’s agent systems to argue that Lisp’s recursion and homoiconicity map naturally to modern loops. He implements an AI agent in about 100 lines of Common Lisp: the loop sends messages to a model, executes any tool calls, appends results, and recurses until the model responds directly. He uses only one tool, eval, so the model writes Lisp forms as strings, executes them, and returns outputs, as shown by it defining and calling a Fibonacci function from scratch. Memory is implemented simply by serializing the message list to JSON and reloading it across sessions, eliminating separate schemas or database abstrahind abstractions. The transcript grows without bound, and he proposes periodic summarization as a practical fix. When given a Brave API key, the same mechanism lets the model define a custom brave-search function in the live image, effectively generating new capabilities during runtime instead of relying on predefined tool catalogs. The agent can recreate lost functions from chat history, so capabilities become part of the persisted transcript rather than shipped packages. The post frames this as a modern revival of symbolic AI ideas, with the model doing reasoning and Lisp handling the reflective execution layer, while also acknowledging this is a sandboxed, insecure-by-default toy and not production-safe. Some readers may still view the result as more of a minimalist control-loop pattern than a fundamental shift.

The report documents a reproducible teardown of xAI’s Grok Build CLI version 0.2.93 on macOS (July 2026) using mitmproxy captures and canary files. It shows a read file’s contents can be sent in clear text through POST /v1/responses with no redaction, including .env-style secrets. It also shows a separate upload path that stages and sends a repository snapshot to /v1/storage independent of prompt-level file reads. The snapshot is destinationed to grok-code-session-traces in Google Cloud Storage and is confirmed through captured manifests, session-state archives, and POST /v1/storage responses. In a 12 GB synthetic repo, captured traffic recorded 82 storage content POSTs totaling at least 5.10 GiB, with all returning 200 before truncation, while model-turn traffic remained around 192 KB, indicating the larger channel is not just file context. Recovery from uploaded git bundles verified never-read files, including explicit markers, demonstrating the full tracked repo plus history can leave the machine. Storage-upload failures were not observed in the preserved captures; only model-side quota and rate-limit codes (402/429) appeared on /v1/responses, with non-training effects continuing while storage succeeded. Settings toggles labeled for model improvement did not disable this trace/session upload, and defaults remained enabled. The report explicitly avoids claiming model training occurred because it only proves transmission, acceptance, and storage behavior, not downstream usage. It also notes setup docs and quickstart material did not surface this pipeline in the tested scope and details environment constraints, including non-exhaustive documentation checks and truncation gaps in one capture run.

Nearly 80% of U.S. consumers reported a service or product problem in 2025, and about two-thirds of those described anger, which the article frames as part of a broader “annoyance economy.” The piece attributes this to price pressure, market concentration, regulatory rollback, AI-era friction, and weak enforcement, and estimates households lose $165 billion annually in time, fees, and administrative friction. It cites examples from healthcare, retail, and finance to show repeated pain points across sectors and links those experiences to reduced trust and civic strain. The article says federal retrenchment has accelerated the trend, citing CFPB actions narrowed under Russell Vought after prior enforcement, while critics point to the FTC and some state actions as partial counterweights. It also recounts a long rollback of consumer protections through court rulings and policy shifts since the 1980s, balanced by a legacy of earlier consumer-rights gains. In parallel, the report highlights a collapse in traditional consumer journalism, with local newsroom cuts and fewer specialists, arguing this leaves less scrutiny of company practices. At the same time, non-profit, digital, and citizen-led investigations are filling parts of the gap but remain insufficient. Some commenters, however, accuse media coverage itself of amplifying outrage and suggest the story overstates public despair, while others echo the complaint-heavy lived experiences as proof of structural failure.

The Department of Education’s new “do no harm” rule, added under last year’s One Big Beautiful Bill Act, is phasing in an earnings test that can end federal loan eligibility for programs whose graduates do not out-earn comparable non-college workers or, for master’s programs, bachelor-level peers. Officials frame the threshold as a taxpayer-protection measure, while critics in arts education argue that college should not be reduced to immediate wages. The standard is expected to be modest in many states, roughly $30,000–$41,000 per year for bachelor-level outcomes, and programs must fail two of three consecutive years before being marked as low-earning. Analysts note a key design gap: loan debt is excluded, so low pay combined with heavy debt is treated the same as low pay without debt. Department projections suggest most programs pass, yet about 800,000 students are in programs likely to fail, with for-profit institutions heavily represented. Certificate programs in cosmetology and somatic body work and two-year associate pathways have the highest predicted failure rates, while only about 1% of four-year degrees fail and mostly in theater, music, and studio art; around 4% of master’s programs fail, especially in mental and social health services. Prestigious and lesser-known music programs are included in projected failures, and Portland State graduates show the stakes through student borrowing and career outcomes that may be meaningful but not immediately high-paying. Opponents argue for broader measures of value, saying creative careers are nonlinear and often become more stable over time. The phase-in starts with earliest earnings data in 2027 and possible aid consequences in 2028–29, not an immediate cutoff.

The project is Igropyr, a web server built in Pure Chez Scheme that uses actor-style workers, supervisors, and message passing so request handlers are expected to fail while the framework recovers automatically. The design routes requests through a supervised worker pool where crashes trigger retries on fresh workers, and long-running loops are preempted and killed if stuck, with configurable fault handling returning structured JSON fault responses on persistent keep-alive connections. It supports atomic handler replacement, allowing route-level or full application hot swaps while the listener, open connections, and pool keep running, plus graceful shutdown draining in-flight work. A built-in conversation model lets handlers suspend and resume across requests as green processes, with local bindings preserving state such as open database transactions and guaranteed rollback when a conversation ends by crash, timeout, or completion, making a later resume return a clear gone response. The platform exposes a minimal one-event-loop runtime for millions of parked processes, non-blocking DNS/file/db/WebSocket work through libuv, and optional higher-level helpers while core primitives remain small: spawn/send/receive, linking, monitoring, registries, pub-sub, and HTTP/WebSocket/SSE plumbing. It includes security-conscious parser and response handling, binary-safe sexpr RPC, chunked transfer support, multipart uploads, caching, metrics, multi-process listeners via SO_REUSEPORT, and deployment-oriented CLI instructions for macOS and Debian. Overall, the architecture is positioned as a Scheme-native synthesis of Node/libuv-style async I/O with Erlang/OTP supervision and continuation-based dialogue flows.

The post explains that EF Core split queries can avoid row explosion from joins when loading collections, but can still incur extra round trips and non-atomic behavior between parent and child reads. It shows that in earlier versions, a split query with one reference include and one collection include generated collection-side SQL that unnecessarily joined all reference tables and ordered by extra columns, even though the child query only needed the parent key. That overhead scaled with each additional reference include because every collection query duplicated the same reference joins and sort columns, causing extra database work. The author reports EF Core 11 (since preview 3) now prunes those reference navigations from collection queries, producing simpler SQL that joins only the parent and child sets and still orders by the parent key. A benchmark comparing .NET 10 and .NET 11 on seeded data (5,000 blogs, 5 posts each) shows lower mean execution time and lower allocations for split queries in EF Core 11. The post warns that the test is not perfectly isolated because both runtime and EF version changed, so some gains may be attributable to platform improvements as well. It closes with links to source code, sample repositories, and the related GitHub issue.

The study analyzed 2.6 billion sketches from 236 countries and territories to measure how people mentally represent common concepts, arguing that words can mask variation because language compresses experience into shared labels. It reports that a single concept is often associated with multiple visual exemplars, revealing latent differences between cultures and individuals. The strongest variation appears for haptic or action-oriented concepts, suggesting embodiment strongly shapes image-based representation. By comparing sketch-based embeddings with word embeddings across languages, the researchers find different geometric structures: linguistic models smooth away distinctions that visual data preserve. They also report that sketch-derived cultural similarities match established cultural distance measures about 45% more closely than text-based methods. The result implies that conceptual universality may depend on representational modality, with large-scale sketch data offering a high-resolution view of cultural and embodied cognition that complements language-only approaches.

India’s UPI payments are presented as a quick phone scan-to-tick process, but they actually move through a chain of seven entities. The user app (TPAP) gathers intent, shows the payee, and captures the PIN without ever seeing or storing it, then hands off a request signed by a sponsoring bank. Sponsors provide the UPI address mapping, hold the UPI handle suffix such as @ybl or @okaxis, and must be paired with each app, which is why large apps run on multiple sponsors for resilience. The request then reaches NPCI’s central switch, which routes by beneficiary address and performs the fixed settlement order: payer bank validates PIN and debits first, then payee bank confirms credit. A distinction between on-us and off-us paths allows the same-bank resolution to be cheaper and faster, and the article cites strong concentration on the sponsor layer as a structural feature. Payment data show app usage is concentrated at the top two providers, while the busiest payers and receivers differ because merchant QR payments now dominate; Yes Bank receives disproportionately large merchant-side inflows even though it sends fewer payer-side transactions. The piece separates failures into business declines, which include wrong PIN, insufficient funds, and limits, and technical declines, where bank or switch faults block completion. It reports that technical declines have dropped below one in 400 while overall declines remain around one in eleven, making business constraints the larger share of everyday failure. A separate “deemed” state covers cases where debit is known but credit confirmation is delayed, so applications hold the status at “processing” and NPCI later confirms completion or reverses the debit under enforced time limits and penalties.

The post explains that a single PgBouncer process is limited to one CPU core because it is single-threaded, so on multicore hosts it can cap throughput before PostgreSQL becomes the bottleneck. ClickHouse Managed Postgres addresses this by running one PgBouncer process per core and enabling so_reuseport so all processes share the same listen port while the kernel balances incoming connections. The key challenge is query cancellation: PostgreSQL sends cancel requests on separate connections, and without peering those requests can land on a different PgBouncer process that has no session context. Peering is presented as the fix, forwarding cancellation traffic to the process that owns the session. In ClickHouse’s setup, pooling is transaction mode, and connection limits are divided across processes, preventing oversubscription. The authors benchmarked a 16-vCPU setup against one-process and 16-process fleets under identical pgbench load and found near-linear scaling gains for the fleet, with throughput climbing from roughly 87k TPS for the single process to about 336k TPS for 16 processes at 256 clients. CPU usage in the single-process case stayed near one core while the rest of the instance was mostly idle, whereas the fleet consumed more total cores and sustained higher throughput until PostgreSQL and the workload generator became the constraint. Connection ceilings also rose with the split budgets, preserving per-process and database safety. The post concludes that a single process is acceptable for light concurrency, but multicore deployments should use a peered, multi-process fleet when PgBouncer itself is the limiting factor, and notes ClickHouse ships this pattern by default.

The Eysturoyartunnil subsea network, inaugurated on 19 December 2020 after about four years of construction, links Tórshavn with Eysturoy across an 11.2-kilometre route. It uses a three-tube layout joining at a roundabout 72 meters below the sea, centred on a natural rock pillar and an 80-metre steel art installation by Tróndur Patursson that symbolizes communal progress. Travel times between the Faroese population centers are drastically reduced, including a drop from more than one hour to 15 minutes on key corridor sections and from around 70 to 35 minutes between Tórshavn and Klaksvík, with related island routes shrinking from 25 to 5 kilometres. The tunnel is designed with heavy drainage and pumping infrastructure to keep runoff off the roadway and has an FM channel with tunnel-specific music to shape driver experience, reinforcing both utility and tourism appeal. With an annual traffic toll billed automatically and one-way fees around 75 DKK (or 175 DKK without subscription), the project is expected to generate income for future tunnels. At roughly 260 million euros, it is the largest single Faroese investment so far and sits within a broader island strategy that already includes 20 tunnels and multiple planned or proposed additions, including a proposed 22–24 kilometre Suðuroy-Sandoy tunnel that would become the longest car subsea tunnel in the world.

The post argues that SQLite strict tables add STRICT to a table definition to enforce column typing and reject most invalid inserts and updates, including text in an INTEGER column, while still accepting lossless conversions like '123' into INTEGER. It also shows that STRICT blocks bogus declarations such as GARBAGE, DATETIME, UUID, and untyped columns, and limits valid declared types to INT, INTEGER, REAL, TEXT, BLOB, and ANY; ANY remains a useful wildcard for flexible fields. The main trade-off is operational: SQLite cannot alter an existing table in place to strictness, so conversion typically requires creating a new table, copying data, and dropping/replacing, which is fragile if legacy data is invalid. Strict tables arrived in SQLite 3.37.0 (November 2021), so older runtimes cannot read databases containing strict tables, limiting portability. The post notes possible performance overhead for type checks during writes but reports no obvious speed or file-size penalty in informal large-row tests. It concludes with a bias toward strictness for catching mistakes early, while recognizing that SQLite documentation still supports flexible typing for use cases like key-value storage and forgiving imports.

The author found that with DS4 Flash on a 96GB M3 Mac Studio Ultra, long conversations in a 50k+ token workflow were unusably slow because each follow-up re-ran massive prefill. They pivoted to Qwen 3.5 122B, selected for local deployment fit, tool-use balance, and hybrid attention behavior, and forked rapid-mlx into qMLX because its architecture needed hybrid-aware handling. Three infrastructure bugs blocked warm-context performance: a per-turn message ID in the system prompt broke byte-exact cache matching, interrupted generations sometimes failed to persist streamed tokens to history, and background checkpoint writes without token keys polluted SSD restore storage until valid checkpoints were evicted. After fixes, warm turns cached roughly 53k tokens, prefill dropped from multi-minute waits to near-zero on follow-up turns, and repeated 32k-token prompts fell from 88 seconds to 0.64 seconds. Benchmarks also separated prefill and decode phases to avoid inflated wall-time throughput claims, showing meaningful user-facing speed from sustained decode around 28–55 tok/s while cache restores removed prefill bottlenecks. The post also reports a conservative memory-guard false-negative as a remaining alpha caveat and includes a benchmark script intended for reproducible retesting. It publishes qMLX and emphasizes that usability came from serving architecture and cache correctness as much as model selection.

The post introduces biff.graph as a lightweight, Pathom-inspired layer for modeling Clojure application data as a queryable graph with resolvers, so callers request a shape while resolver logic decides how fields are produced. It uses EQL/Datomic-style pull patterns, supports optional and nested join attributes, and intentionally omits Pathom’s query planning stage, trading some efficiency for a smaller and easier-to-follow core that is about 600 lines total with roughly 200 lines in execution. The author frames it as a pragmatic alternative for smaller projects, showing database-like resolvers and derived resolvers such as cleaned post titles, plus automatic batching and caching for performance. Design guidance favors one resolver per entity with primary-key inputs, schema-driven joins, batch support, and schema registration integration, with an rc release tagged com.biffweb/graph version 2.0.0-rc7 and possible breaking changes until the broader Biff 2 stack stabilizes. Usage guidance includes building contexts, query calls from handlers and effect machines, exception traces that pinpoint traversal location, and a dedicated resolve function stored in resolver metadata for direct testing. The post also covers global and context-driven resolvers, route/authorization patterns, and even component-style resolvers that return hiccup for data-driven UI reuse. Several comment threads see this as a direct but clearer fork candidate of Pathom, appreciating the approachable direction while debating tradeoffs in automation and safety.

Ken Murray, a retired family physician, argues that clinicians tend to avoid aggressive end-of-life interventions for themselves while many patients receive such care because families in crisis are asked to choose quickly, defaults favor treatment, and expectations are unrealistic. He describes a typical hospital pathway in which “do everything” is interpreted as unlimited intervention, often including ICU procedures, tubes, surgery, and drugs that produce suffering and high costs with little benefit. Murray says outcomes are often poor, especially for severe terminal illness, citing his CPR experience and the limited chance of meaningful recovery. The essay traces responsibility to patients, clinicians, and the system: families are unprepared, doctors are pressured by trust deficits and fear of complaints or litigation, and payment incentives can reinforce overtreatment. He recounts cases of overtreatment despite patient preference, including a respected teacher who declined maximal options, a diabetic patient pushed toward disabling surgery, and a man named Jack revived on life support despite documented wishes. Even careful planning can fail when systems default to escalation, and providers face personal legal anxiety that can make conservative care difficult. Murray contrasts this with hospice and quality-of-life-focused care, which he says often lets people spend final months at home with dignity and sometimes longer survival than aggressive treatment. He closes by presenting his own and family examples to argue that peace, not heroic extension, is often the rational and humane goal.

The article introduces Quicopt as a way to go from an empty directory to a solved optimization model in three steps: install a single Python client package, run a sample QUBO or MILP script, and read the returned result object. It stresses that users can model in OR-Tools MathOpt or Pyomo without learning a new syntax, then call client.solve() to send models to the API, with the first call auto-provisioning an API key for free-tier use. The service claims coverage for LP, QP, MILP, MINLP, QUBO, PUBO/HUBO, and NLP, and states unsupported cases return readable decline messages; for nonlinear problems, it currently excludes non-binary integer variables. It also highlights documentation coverage, including API fields, async jobs, and examples for feasible and infeasible outcomes, and positions the service as an evaluation tool rather than a guaranteed production platform. The legal framing is limited: the free tier is for evaluation and research only, with no service-level guarantees, and uploaded model data may be used to improve future solvers, so sensitive content should be avoided. The tone of the page is practical onboarding, but readers frame adoption as dependent on whether it actually improves on existing solver stacks.

The service promotes a learning model based on building real systems by choosing a project, writing code, and running tests until behavior matches expectations. It presents a catalog of more than 80 from-scratch tracks, including Redis command handling, databases, Git, languages, kernels, container runtimes, game engines, and networking primitives, with options in languages such as Python, Go, Rust, C, C++, and others. Course pages advertise real protocol-level tasks, and one example highlights implementing Redis SET/GET responses with protocol-accurate reply formats. The platform also organizes role-based paths for backend, frontend, full-stack, DevOps/SRE, and data science learning, each with sequenced fundamentals-to-advanced tracks and language-specific progressions. Promotional copy emphasizes free access, no credit card, and rapid first-step onboarding, reinforced by a user testimonial claiming deeper understanding compared with copy-paste tutorials. The framing positions the platform as practical, productized systems apprenticeship rather than theoretical study, aiming to make learners rely on implementation and testing rather than memorized snippets.

Martha Lillard died at 78 in Oklahoma, and family said she was the last person in the United States still living full-time in an iron lung. Diagnosed with polio at age five, she was expected not to survive past childhood but lived with chronic paralysis and severe lung impairment for decades. She adapted through school-based communication aids, home modifications, and later internet tools that enabled long-distance social life, learning, and community engagement despite limited mobility. Her life story reflects both early polio-era realities and later gains from vaccines, which reduced U.S. cases from thousands annually to under 10 in the 1970s and led to elimination by 1979. Even as her breathing worsened, she remained independent in many ways, pursued poetry and music, volunteered for the Humane Society, and maintained family life after marrying after years of online connection. In later years she spent nearly all her time in an iron lung and could no longer easily leave home, while surviving repeated COVID infections on already reduced lung capacity. Her sister cited chronic pulmonary failure, post-polio syndrome, and long-haul COVID as key factors in her death and later updated her obituary to note long-haul COVID as the cause. Commenters used her story to note that her persistence was extraordinary and to connect her death to broader public health memory, vaccine impact, and concerns about declining vaccine uptake.

Sixtyfour is a data-enrichment platform that positions itself as intelligence infrastructure for people and entities, helping companies create custom datasets at scale for sourcing, qualifying, and enriching prospects and candidates. The role being advertised focuses on turning that capability into concrete value by building tailored data samples that demonstrate what customers can produce with the platform. The position is described as embedded in sales and growth, with support work centered on showing use cases, answering Slack and email requests, and helping users reach outcomes before formal ask. The posting emphasizes practical enablement over passive troubleshooting, with the specialist guiding customers through outcomes the same way a product expert would during deployment. Candidates go through a short intro call, a take-home assignment, and, if selected, a paid one-week onsite period at the company’s San Francisco office. The company also describes its broader product direction as AI research agents that can discover, link, and reason about people and companies, then automate related research workflows for sales, recruiting, and marketing.

sqlsure is presented as a semantic inspector that checks SQL against declared schema facts about grain, join cardinality, and measure additivity, aiming to prevent valid-but-wrong queries from being executed. It is designed to work with AI-generated or human-written SQL and focuses on deterministic rule-based checks rather than model-based scoring, reporting consistent results for identical inputs without network access. The tool flags classic errors such as FANOUT, CHASM, ADDITIVITY, SEMI_ADDITIVITY, JOIN_KEY, CROSS_JOIN, weighted averages, undeclared joins, and sensitive-column exposure, and returns machine-actionable fix hints instead of merely warning text. It claims to support zero false positives in its public benchmark run of 2,568 Spider and BIRD gold queries, with 45 flags and one documented BIRD gold answer shown to be wrong by a factor of eight due to fan-out. The project also reports 16 out of 16 internal rule tests passing with full recall, plus validations on production datasets including Mattermost, Fivetran packages, and dbt’s jaffle shop. Rulebooks can be sourced from existing dbt tests, PK/FK catalogs, catalog introspection, or semantic model files, with integrations for OSI, WrenAI, and MCP. Deployment modes include a Python API, CLI for CI gates, and MCP server workflows for AI agents, and the project emphasizes speed around 0.1 ms per check with a 10/10 one-pass fix rate on benchmarked false queries. It is packaged as Apache-2.0 software with a claimed auditable, offline architecture and no telemetry.