The author argues that as AI-generated content floods the web and bot browsing surpasses human activity, care-made human creations and small personal web spaces feel more important than ever. They suggest AI and automated browsing create an opening for new collective visions of the web, and describe their projects wewere.online and playhtml, which bake human presence into browsing and let any website become a live shared space.
The article discusses whether AI-powered reverse-engineering tools could let someone convert the PS5 version of GTA VI into a PC port, and whether that might push Rockstar to release an official PC version sooner. Vermeij told Rock Paper Shotgun that such an effort could cost "tens of thousands of dollars in tokens" and likely wouldn't match Rockstar's own work, but might be good enough for some PC players. While there has been no official timing announcement for the GTA VI PC port, reports suggest Rockstar is aiming for the first quarter of 2027.
In this Vergecast episode, Nilay and David discuss how the 2026 midterms, in which voters backed Democrats and voted against AI, will affect the tech industry, including harder fights over data centers and likely congressional pressure on tech CEOs and AI regulation. They also examine whether anyone has figured out how to make money on AI, questioning whether OpenAI's web search growth matters when even Google struggles to monetize AI search, and note that businesses are scaling back and agents don't appear to be taking over.
An Ask HN post notes that eight months ago EXO labs promised fast inference on local hardware, but it is not fast enough: getting a response through hermes using Kimi K2.6 takes a minute and a half. The poster asks whether there is hardware a developer can buy that can actually offset cloud bills.
The author argues that "billing" bundles distinct jobs—usage metering, pricing, balances, invoicing, revenue recognition, and contracting—and that AI companies should own the customer-facing fast loop (event collection, aggregation, pricing, and balance pools) while buying specialist software for invoicing and revenue recognition. End-to-end billing vendors often require customers to run their own collection infrastructure, manage enterprise agreements in parallel systems, and rely on spreadsheets for ASC 606 revenue recognition, and replacing one outgrown component can force changes across a monolithic stack. The recommended architecture connects metering and back-office systems through a single shared model of commercial agreements owned by the back office.
This post profiles Fabian Theis of Helmholtz Munich, whose lab has pushed algorithms for multi-scale, multi-modal, time-varying biological data and helped shape the Virtual Cell concept. It notes that a 2025 Arc Virtual Cell Challenge showed constant baselines often beat sophisticated perturbation-prediction models, though better-calibrated metrics let deep learning outperform the mean baseline, and that Theis argues existing datasets make perturbation prediction too easy a problem. It frames the roughly $1.8B Biohub commitment from Biohub, NIH, DOE, Google and Meta as funding large, high-precision, multi-scale data collection to predict evolution across molecules, cells, niches, tissues, organs and organisms.
This post compiles a reference list on AI in education, citing Risko & Gilbert (2016) on cognitive offloading, Fütterer et al. (2026) in Nature Human Behaviour on AI as a learning coach, and Bastani et al. (2025) in PNAS on generative AI without guardrails harming learning in high school mathematics. It also cites Bjork & Bjork (2011) on desirable difficulties, Universities UK's Future jobs roadmap (2026), the World Economic Forum's Future of Jobs Report 2025, a Stanford Digital Economy Lab working paper by Brynjolfsson, Chandar and Chen (2026), and a Times Higher Education article (2026) reporting that most employers have no plans to replace graduate roles with AI.
Blaise Agüera y Arcas argues that Descartes' mechanical philosophy severed body from soul, leaving behind three "monsters"—the homunculus, the ghost, and the philosophical zombie—and draining agency, purpose, and meaning from the world. He contends that large language models, which learn and generalize rather than merely imitate by rote, undermine Descartes' claim that machines could never reason or use language, and that internal representations in these models often mirror ours. He calls for healing this "Cartesian wound" so that agency, purpose, and value return as the basis for reciprocal relationships and mutual understanding.
This piece discusses Fabian Theis's work on algorithms for multi-scale, multi-modal, time-varying biological data and the Virtual Cell concept, alongside the recent $1.8B Biohub commitment from Biohub, NIH, DOE, Google and Meta. It notes that the 2025 Arc Virtual Cell Challenge showed constant baselines often beating sophisticated perturbation-prediction models, though a cited paper argues better-calibrated metrics let deep learning outperform the mean baseline. Theis argues perturbation prediction on existing datasets isn't hard enough to warrant complex algorithms, and that focus should shift to large, high-precision, co-registered data spanning molecules through organisms.
The author hosted Bill Gross, founder and chairman of Idealab, for a conversation with the Exponential View community about building businesses in the AI age. Gross recently founded Idealab.ai, which uses AI to explore potential business ideas, evaluate them against real-world problems, and help founders pressure-test and refine them. The author highlights Gross's emphasis on iteration and speed, noting that AI can generate ideas and handle more of the mechanics but founders still need to listen to customers, exercise judgment, and adapt.
The post notes that yesterday's $2.5 trillion market drop was only part of the bad news, alongside a Bank of England study finding that AI is again not producing the expected productivity gains. The full post is behind a free-subscription prompt.
A Hacker News post argues that what people call AI intelligence is essentially mathematics mirroring a dataset, and asks how intelligence should be defined, including whether AGI refers to observer intelligence. The post links to its comments at https://news.ycombinator.com/item?id=50392695.
A Misaligned Mag article asks whether avoiding AI could become a constitutional right, shared on Hacker News with 3 points and 2 comments.
The article describes knowledge workers who now spend their days managing and reviewing the output of AI agent "swarms" that draft copy, write code, and produce strategy, and argues that when every company in an industry runs its thinking through the same small set of LLMs, competitive differentiation erodes and skills atrophy. Drawing on the Kissimmee River straightening, Kyle Chayka's "Airspace," and research on LLM homogenization, it predicts a barbell-shaped future in which cheap high-volume AI output and premium verifiably human craft both thrive while the middle hollows out.
The author argues that AI is the costliest new technology ever created, with WSJ reporting it is the biggest economic bet in US history as a share of GDP, and that beyond financial costs it rapidly exacerbates climate change, economic precarity for the middle class, and institutional instability. They contend LLMs are a genuinely useful new computing interface but that the speed of the buildout denies society time to identify and regulate emergent harms, and that deskilling knowledge work makes workers more replaceable.
Johannes Treitz recounts replacing a Hotwire Native wrapper for his Werkbook app with AI-agent-built native Swift and Kotlin apps, arguing that when agents write most of the code, cross-platform frameworks' abstraction layers no longer justify their limitations. He notes Shopify announced in September that "native is now the future of mobile at Shopify," citing AI agents, and rebuilt its Shop app in Swift and Kotlin in twelve weeks, with Coinbase apparently next. He keeps the Rails API as the security perimeter, treating the mobile apps as untrusted clients.
After Democrats won the midterms, more than $2.5 trillion was wiped off the Nasdaq as investors fled the AI trade, and US voters passed 43 of 44 local measures to slow data centers. The article argues this backlash is leverage rather than a death knell, citing DataVita's Lanarkshire deal to pay up to $100,000 per megawatt per year to a local community fund, and contrasts it with Oracle's smaller New Mexico package. It notes data centers create few permanent jobs per billion dollars spent and urges counties in grid queues to negotiate more.
An Ask HN post poses the question of how readers will invest now that the AI bubble is said to be bursting. The submission links to its Hacker News comments page and had 5 points and 5 comments at the time of capture.
A YouTube video featuring NYU professor Aswath Damodaran is titled "The AI Bubble Lesson Is Coming." The item was submitted to Hacker News, where it had 2 points and no comments at the time.
An Ask HN post asks whether distractions from non-technical colleagues have increased now that engineers are developing most of their code with AI at a rapid pace, and whether it is easy to answer their requests while a coding agent is working. The post links to its Hacker News comments page.
An SCMP article argues that a Democratic sweep could slow AI growth in the US and thereby help China's ambitions. The supplied material contains only the headline and link, with no further details.
This article surveys how AI coding agents have moved from autocomplete-style assistants to tools that can inspect repositories, modify multiple files, run commands and tests, and iterate on their own changes, citing JetBrains and Stack Overflow survey figures on adoption. It argues that faster code generation does not automatically mean faster delivery, pointing to METR's 2025 study in which experienced developers took 19% longer with AI allowed while believing they were faster, and stresses review, security, and human judgment. It also distinguishes prototypes, MVPs, and production-ready products, and offers guidance for introducing agents into workflows and for clients evaluating AI-assisted teams.
The author argues that science fiction is descriptive rather than predictive, then scores famous stories against how AI was actually built, what it could do, and how it was deployed. Ted Chiang's "The Lifecycle of Software Objects" ranks first for its account of teaching, maintaining and selling AI, followed by Heinlein's "The Moon Is a Harsh Mistress" and Ken Liu's "50 Things Every AI Working with Humans Should Know." The author found no pre-2017 work that clearly describes the transformer attention mechanism or the sequence from chatbots to agents capable of novel mathematical results.
This keynote argues that differing product role definitions stem from two operating models: the project model, which yields feature team product managers and agile product owners focused on delivery, and the product model, which yields empowered team product managers focused on product discovery and outcomes. It contends that agentic AI is automating delivery work, making product owner and feature team roles less relevant while increasing demand for empowered team product managers skilled in discovery, especially for probabilistic AI products. It cites Shreyas Doshi on the distinction between product managers focused on output versus outcome.
Village's LLM Atlas finds closed models hold almost every budget on the price-performance frontier, with open-weight models the best buy at only 3% of budgets, the lowest share measured, down from 41% in June. DeepSeek V4 Flash and Zhipu's GLM-5.2 once led large budget bands, but OpenAI's GPT-6 Luna and GPT-6.1 Terra and Anthropic's Claude Haiku 5.5 reclaimed them. The piece attributes the shift to closed labs distilling their own frontier models, releasing full model families, and serving inference at greater scale.
A narrative essay follows Ravi, an IIT Madras graduate doing systems implementation for a multinational outsourcing firm in Bangalore, as AI agents take over his team's work and his division is cut by half amid layoffs and unrest. An accompanying overture argues that the massive capital expenditure on machine intelligence is not a bubble, and that as with Gutenberg's press, oil, the transistor and railroads, the wealth created by a transformative technology accrues not to its inventors but to those who control the means of conduction.
Prof. Gheorghe Ioan Mihalas argues that medical education has been hijacked by "quick fix" AI courses that teach prompt-typing and slide generation while stripping away theoretical foundations, training clinicians to operate opaque technologies without understanding their limits. He contends this produces an "illusion of knowledge" that threatens patient safety, erodes clinical authority as patients arrive with AI-generated demands, and normalizes acceptance of hallucinations and bias. He calls for mandatory, enforceable three-tiered AI education tailored to students, practicing clinicians, and hospital leadership.
After midterm wins driven partly by voter opposition to data centers and AI accelerationism, Democrats face the challenge of regulating AI, and the author warns they risk repeating social media's regulatory failures by lacking a coherent goal, a backup plan, and cross-group coordination. The piece notes that 40 of 41 data center-related ballot measures went against data centers, and that Congress may have far less time to act on AI than it did on social media.
On Thursday 5 November 2026 the Nasdaq Composite fell 5.52% to 24,315.00 and the Philadelphia Semiconductor Index about 11%, after midterm elections gave Democrats both chambers of Congress and voters in ten states rejected most local data-centre measures, which investors read as a cap on the US AI build-out. The selling hit a market already uneasy about AI capital spending outrunning cash flow, distressed-level credit at GPU cloud providers and 10-year Treasury yields near 5%, with more than $3 trillion of Nasdaq market value gone by midday on Fortune's count. The author, who runs product and engineering at an AI company, argues the market repriced the money and concrete behind the build-out while demand evidence did not move, citing AMD's above-expectation guidance and Anthropic's $17 billion third-quarter revenue.
The article argues that open models are bottlenecked by hardware because Mixture of Experts architectures must keep all total parameters in memory even though only a fraction are active per token. It proposes a frozen, quantized base model paired with thousands of 2–5 MB low-rank adapters selected by a two-step hierarchical router, plus Multi-Head Latent Attention to compress the context cache by roughly ninety percent. Because small adapters can be streamed from system RAM or SSD in a millisecond or two, the design lets capable AI run locally on accessible hardware instead of multi-GPU clusters.
A Hacker News poster asks how pair programming and mob/ensemble programming will evolve or disappear in the age of agentic AI, noting their team used to run mob sessions over Zoom with a driver, navigator, note-taker and observers. They found mob programming draining and pair programming somewhat better, and argue that with agents it no longer makes sense to gather everyone on a call to watch someone else's agent work.
A CACM blog post, "The Real Cost of AI Code Shows Up 30 Days Later," is discussed on Hacker News, where the submission has 3 points and 1 comment.
NFTs are worth up to 97% less than at their January 2022 peak, with global sales this year under $2 billion, and INSEAD's Ben Charoenwong expects them to persist only as a niche collectible market. Andrea Barbon of the University of St Gallen sees possible futures in tokenizing real-world assets and in giving AI agents unique, verifiable NFT identities, as well as letting creators tokenize work to track AI training use. The article cites the Bored Ape Yacht Club, including Justin Bieber's purchase that fell roughly 99% in value, as emblematic of the decline.
In an interview with Game*Spark, Hideo Kojima said AI has made game development more efficient and deadlines easier to meet, but he worries that unrestricted AI could eventually take a key role in creative fields and remove the enjoyment of the creative process. He compared creation to model kits, jigsaw puzzles and LEGOs, where the building process is the fun, and said he doesn't want a future where that trial-and-error is lost, though he expects things will be fine for the next five to ten years.
This blog post argues that AI has made building products cheap and fast, but the harder discipline of testing whether anyone wants them has become easier to skip, citing Zappos' low-cost shoe experiment, Humane's $699 AI Pin, and a founder who shipped 128 releases with zero external users. It urges founders to write a one-sentence hypothesis, define success and failure before shipping, and set a kill date.
A developer's LiveKit issue report found that 14 of 75 appointment-cancellation requests were not delegated to the backend within six seconds, and five were never delegated, even though the agent sometimes told callers the cancellation was underway. The post argues that GPT-Live's continuous voice frontend decides when to hand work to a reasoning backend, so delegation failures, overlapping-speech confirmations, and intent changes can leave backend intelligence and tools unused, while the closed model cannot be fine-tuned to fix them. It recommends measuring time to first substantive information and verified task completion rather than time-to-first-audio alone, and notes that preventive speech guardrails add latency and playback-state complexity.
Paul Ford is quoted: "The web browser is very, very, very powerful software for applications, and the cost of accessing that power is going down seemingly every day." The quote is dated 5th November 2026 and attributed to his piece "The Web Is More Than Enough."
A blog post by Mathew Ingram argues that AI should be trained as though it is conscious, just in case it is. The post is linked from Hacker News, where it had 2 points and no comments at the time of the data.
This post argues that remotely hosted personal AI Agents act as intermediaries between Users and Sites, stripping Sites of the identity, reputation and accountability signals they rely on for security, fraud prevention and monetization. It proposes the Intermediation Principle and surveys emerging standards — Web Bot Auth, MOLE, OAuth, AIPREFs and the new Trusted Agent Delegation coalition — while noting Cloudflare's new tools for seeing Agents, setting per-Agent access policies, and tying Agents back to users via Account Abuse Protection.
Gary Marcus recalls being criticized on X for predicting tech stocks would crash once AI companies lost their free ride, noting that the following day's market was mixed, with chipmakers and neocloud firms like CoreWeave hit hard while Amazon, Apple and Google bounced. He cites theories that investors were rotating into larger tech companies or expecting hyperscalers to curb AI data-center capex, partly due to public resistance to data centers and election results. He says today looks different: "It's a slaughter. Almost no company is exempt."