The hottest Technology Substack posts right now

And their main takeaways

Links For December 2025

Astral Codex Ten • 18651 implied HN points • 10 Dec 25

AI is now the dominant political and technological battleground, driving fights over regulation, funding, and geopolitics like chip exports and PAC spending.
Many hyped tech and biotech ventures make grand claims and show warning signs of fraud or shaky science, so investors and users should be skeptical and favor proven alternatives.
AI’s spread will upend jobs and even the role of wealthy capitalists, creating pressure for redistribution or new power dynamics, so governments need better transparency, auditing, and realistic regulation.

The Markets' AI Guessing Game, Moltbook Explained, AI-Induced Layoffs?

Big Technology • 3377 implied HN points • 02 Feb 26

🕹 Technology Artificial Intelligence Social Networks Market Dynamics Corporate Earnings Workforce Impact Startup funding Public Policy

The market doesn’t know who will win the AI race, so small earnings details or capex moves spark huge stock swings and sustained volatility.
Moltbook shows what an agent-driven social layer could look like, but most posts aren’t truly autonomous and the platform raises real moderation, impersonation, and security worries.
Layoffs branded as ‘AI-induced’ often reflect firms acting on AI’s anticipated future impact rather than current performance, so AI is a factor but not always the direct cause.

Many tokens make all bugs shallow & open source’s new maintainers | Chainguard's Dan Lorenc

Dev Interrupted • 42 implied HN points • 17 Mar 26

🕹 Technology AI Software Security Open Source DevOps Cloud

Token costs for AI tools are an operational expense employers should cover, not a substitute for pay; companies need to provide the compute and subscriptions engineers need to do their jobs.
Agent-driven development requires treating agents like workers you manage—set up harnesses, clear guardrails, and plan carefully so AI-generated work doesn’t create technical debt.
The rise of agents reshapes risk and the ecosystem: expect permission and outage problems, new markets that sell to bots, and pressure on open source maintainers unless automation helps sustainably fill the gap.

AI #155: Welcome to Recursive Self-Improvement

Don't Worry About the Vase • 3225 implied HN points • 12 Feb 26

🕹 Technology Artificial Intelligence Machine Learning AI safety AI Policy Industry Trends Compute Infrastructure

AI capabilities are accelerating rapidly, with new model releases improving agentic coding, in-context continual learning, and media generation so fast that benchmarks and measurement struggle to keep up.
These advances are already reshaping economies and work: automation and agentic tools threaten many jobs, trigger volatile market reactions, and push companies toward new monetization and product strategies like ads and verticalized offerings.
Safety, alignment, and governance remain urgent unresolved problems; researchers are worried or leaving, red lines get crossed, and connecting powerful models to real-world systems (labs, agents, surveillance) creates legal and existential risks we aren’t yet managing.

My take on data quality

Minimal Modeling • 304 implied HN points • 15 Mar 26

🕹 Technology Data Modeling Data Quality Data Engineering Databases ETL SQL

Treat queries as functions and start by defining anchors: maintain a compact one‑column list of unique IDs for each entity and document retention/archive rules so input data quality is clear.
Represent attributes and links as clean two‑column datasets (anchor ID + value or anchor ID + anchor ID), filter out NULLs and sentinel values, canonicalize values, use only atomic types, and ensure uniqueness.
Materialize those compact datasets and keep them updated with a pipeline so your data is correct by construction; from these trusted pieces you can build flat tables while avoiding common issues like duplicates, unclear identity, and messy JSON.

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China has invented a whole new way to do innovation

Noahpinion • 18765 implied HN points • 04 Dec 25

🕹 Technology Innovation R&D Tech Policy Manufacturing Commercialization Science Funding

Innovation is a pipeline that moves from broad scientific ideas to specific sellable products, with universities, government labs, corporate R&D, and manufacturers each playing different roles and often handing work off across countries.
China has built a highly vertically integrated, state-coordinated “whole-nation” system that links funding, research, and industry to control the entire innovation chain from basic science to commercialization.
That system has produced huge R&D spending, rising high-quality scientific output, manufacturing dominance, and growing licensing revenues, meaning China is turning research money into marketable technologies faster and reshaping global tech competition.

Backfills - The Necessary Evil of Data Engineering

SeattleDataGuy’s Newsletter • 906 implied HN points • 23 Feb 26

🕹 Technology Data Engineering Data Pipelines Data Quality Data architecture DevOps Cloud Infrastructure ETL

Backfills are an unavoidable part of data work — you need them when source data is corrected, pipelines have bugs, or schemas and logic change.
They’re hated because they can be expensive, slow, and risky at scale, can disrupt downstream users, and erode stakeholder trust when numbers shift unexpectedly.
Design for safe backfills by building parameterized, rerunnable pipelines, adding strong data quality checks, communicating changes clearly, and using table-swaps or other strategies when partitions or immutable storage formats make in-place fixes risky.

Anthropic’s New AI Report Accidentally Reveals an Industry-Sized Weak Spot

The Algorithmic Bridge • 828 implied HN points • 06 Mar 26

🕹 Technology Artificial Intelligence Machine Learning Jobs Labor markets Productivity Adoption Industry Trends

A metric that mixes LLMs' theoretical abilities with real-world usage reveals a huge gap between what models could do and what they're actually used for. For example, models theoretically cover ~94% of computer/math tasks but are used for only ~33%, and a similar gap appears in legal work (~90% vs ~20%).
There are two ways to read this gap: one is optimistic that adoption will expand until real use matches theoretical capability, and the other is that the gap shows real limits and inflated lab benchmarks rather than a temporary lag.
The practical lesson is that the industry may be overestimating AI's near-term labor impact and needs to focus on rigorous evidence of real-world competence and adoption, not just benchmarked capabilities.

AI #156 Part 2: Errors in Rhetoric

Don't Worry About the Vase • 2060 implied HN points • 20 Feb 26

🕹 Technology AI Policy Safety Ethics Governance Research Robotics

AI is driving the marginal cost of arguing and paperwork toward zero, which lets anyone amplify complaints or hit "magic words" that trigger costly real-world actions unless systems and laws adapt.
Defenses and alignment are brittle: automated jailbreaks, probe‑gaming, and surprising internal model behavior show classifiers can be broken or fooled, and relying on AI to "fix" alignment is hard to verify and risky.
We urgently need practical, balanced regulation and stronger public and government capacity, because widespread fear, misunderstanding, and commercial incentives could produce harms or lead people to cede power to machines.

Do Code Agents Actually Understand the Code They're Working With?

Gonzo ML • 315 implied HN points • 13 Mar 26

🕹 Technology AI Machine Learning Software Engineering Architecture Developer Tools Benchmarks

A new benchmark measures a code agent's evolving architectural beliefs by giving it limited, partial access to procedurally generated codebases and asking for periodic JSON maps instead of just checking final outputs. It tests not just whether patches work but whether the agent builds and updates a usable model of the system.
Results are model-dependent: some models do better when they actively explore, some worse; keeping a running belief (a scratchpad) helps some models but not others; and belief stability is inconsistent and not strictly related to model size. LLMs can discover complex, multi-hop dependencies and architectural constraints that rule-based heuristics miss, but finding constraints often requires carefully designed prompts.
This is an early v0.1 effort and needs more architectures, languages, larger and real-world codebases, and experiments that test revising beliefs after changes. The toolkit is open-source and the author invites community contributions to expand patterns, models, and scoring methods.

Vibes is all you need

Alex Ghiculescu's Newsletter • 203 implied HN points • 19 Mar 26

🕹 Technology AI Software Product Engineering SaaS Development

Modern AI can write, test, and interact with your app autonomously, which removes many traditional engineering bottlenecks. This shifts the product vs engineering balance and pushes lead engineers to focus on shipping end-to-end and building the right architecture.
To adopt this, try the tools on real bugs, run hackathons to show what’s possible, give everyone access to AI coding tools, and set an AI budget so teams don’t hesitate to use them. These practical steps lower friction and expand what people will attempt.
Rethink product strategy and jobs-to-be-done: use AI to tackle ideas that felt too hard, cure writer’s block, and automate tedious gruntwork. Aim to build features that fully solve customers’ jobs rather than just incremental pieces.

Olmo Hybrid and future LLM architectures

Democratizing Automation • 364 implied HN points • 05 Mar 26

🕹 Technology AI Machine Learning LLMs Model Architecture Open Source

Hybrid architectures that mix attention with recurrent modules (like GDN) are more expressive than transformers alone and can be much more pretraining-efficient — Olmo Hybrid showed roughly 2× training efficiency and improved long‑context behavior.
Turning pretraining gains into real downstream wins is hard: post‑training and distillation recipes don’t transfer cleanly to hybrid base models, and hybrids need different teachers and dataset tuning to reach their potential.
Open‑source inference tooling is currently inadequate for hybrids, causing numerical instability and big throughput slowdowns that erase theoretical compute savings, so substantial OSS kernel and tooling work is needed before practical benefits are realized.

Microsoft's AI Strategy Deconstructed - from Energy to Tokens

SemiAnalysis • 21315 implied HN points • 12 Nov 25

🕹 Technology AI Cloud Computing Data Centers Chips Software

Microsoft initially led the AI market but faced challenges after pausing their datacenter expansion and slowing commitments to OpenAI. This gave competitors like Oracle and Amazon an opportunity to secure more contracts directly with OpenAI.
Microsoft is now ramping up its investments in AI and datacenter capacity again, aiming to meet growing demand. They are also exploring various methods to boost their AI capabilities, including using custom chips and expanding their infrastructure.
Despite their efforts, Microsoft faces stiff competition and must improve their cloud services to cater to AI companies. They need to refine their offerings to stay relevant and capture more of the growing AI market.

Marissa Mayer: I Am a Software Girl

Magic + Loss • 238 implied HN points • 23 Oct 24

🕹 Technology AI Software Innovation Entrepreneurship Women in Tech

Marissa Mayer sees AI as a bright and helpful force in our lives, rather than something dangerous or negative. She believes it can enhance family and social experiences.
She has a strong opinion against feminism, feeling it is too militant and not focused on merit. She thinks being a geek is more important than gender roles.
Mayer enjoys various topics like fashion and art, showing that she has a diverse range of interests outside her tech career.

Two takes on AI and the future of America

Marcus on AI • 11461 implied HN points • 23 Dec 25

🕹 Technology Artificial Intelligence Semiconductors Data Centers Productivity Investment Infrastructure

Huge bets on large language models have driven a boom in chips and data center construction, but real-world performance and trust are lagging, so those assets could become overvalued and risky.
Multiple studies and company experiences show generative AI often fails to deliver the promised productivity gains and can sometimes harm outcomes, so it’s premature to treat it as a guaranteed productivity revolution.
Putting an entire economy or national strategy all-in on generative AI is dangerous; diversification and cautious risk management are needed to avoid big losses or calls for bailouts.

What Are Chinese People Vibecoding?

ChinaTalk • 948 implied HN points • 24 Feb 26

🕹 Technology AI Software Developer Tools Mobile Apps App Store Startups

Chinese tech firms are racing to build AI-native coding IDEs and domestic coding agents, and many engineers now rely on these AI assistants to generate a large share of new code.
Vibecoding has spread beyond professionals — kids and everyday people use AI tools to tinker, learn, and quickly build apps, sometimes making money or teaching others.
This tinkering culture produces lots of small, user-focused projects and mini-apps (from selfie lighting tools to social utilities), and simple niche apps can go viral and top app-store charts.

✨ The end of work? Not yet—maybe not ever

Faster, Please! • 1919 implied HN points • 22 Feb 26

🕹 Technology AI Automation Jobs Labor markets Politics Economics

People are scared that AI will automate white‑collar jobs and trigger massive unemployment, especially if office tasks like contracts and accounting are quickly automated.
Those apocalyptic scenarios have become a popular genre, but it’s worth stepping back and not assuming the end of work is inevitable.
Whether or not human‑level AI appears soon, AI’s spread will shape politics and policy — the 2028 election and debates about incomes, regulation, and oversight will likely revolve around it.

Lost in Space

Doomberg • 7051 implied HN points • 06 Jan 26

🕹 Technology AI Cloud Space Energy Infrastructure Hardware

Companies are proposing orbital data centers that would use uninterrupted solar power, fleets of satellites with solar arrays, optical links, and AI accelerator chips to handle energy-hungry model training off Earth.
The idea neatly fits the current AI investment craze and could attract big investor and banking interest, but such futuristic pitches can be speculative and sometimes resemble hype more than practical business plans.
Practical constraints — notably a major cost/feasibility factor only briefly acknowledged — likely make space-based data centers uneconomic or impractical compared with terrestrial server farms for the foreseeable future, based on basic calculations.

The First Trialectic - "Can Computers Teach Us to Act in Good Faith?"

Heir to the Thought • 159 implied HN points • 25 Oct 24

🕹 Technology Human-computer interaction Digital ethics Artificial Intelligence

The Trialectic is a new debate format involving three speakers to encourage richer discussions. It shifts the focus from winning to collaborative learning, allowing participants to explore diverse perspectives.
Computers cannot teach us directly about good faith, but they can influence how we understand and engage with it. They can help identify bad faith through structural guidelines and data-driven insights.
Having open and honest conversations is essential for improving trust in discussions. Recognizing that communication is complex helps us navigate different interpretations and encourages understanding among participants.

Speed Trap

Cloud Irregular • 1330 implied HN points • 19 Feb 26

🕹 Technology Autonomous Vehicles Transportation Law enforcement Public Policy Societal Impact

Self-driving cars cut down on human speeding, which can wreck towns that rely on traffic fines for most of their income.
Attempts to block or confuse autonomous vehicles usually fail as the tech and laws adapt, so towns have to scramble to find other ways to fund themselves.
Passengers often don’t know how fast an autonomous car was going, and that uncertainty can be used by police or municipalities to keep generating enforcement revenue.

222. Automating Processes with Software is HARD

Hardcore Software • 1686 implied HN points • 03 Oct 24

🕹 Technology Automation Software Engineering Artificial Intelligence

Automating processes is often harder than people think. It's not just about making things easier, but figuring out how to handle all the unexpected situations that come up.
Most automation systems are fragile and can easily break if inputs or steps aren't just right. This makes dealing with exceptions, rather than routine tasks, the real challenge in automation.
The future of automation might not be about fixing the tasks we already have. Instead, it could lead to new ways of doing things that we haven't thought of yet.

Qualcomm's RISC-Ventana Fusion

The Chip Letter • 6770 implied HN points • 13 Jan 26

🕹 Technology Semiconductors Chip Design M&A

Qualcomm bought Ventana mainly to add experienced RISC-V CPU engineers and IP so it can accelerate its own CPU plans and reduce reliance on Arm.
Ventana produced promising Veyron CPU designs (V1 then V2 with vector support) but appears to have struggled to convert that tech into clear customer wins.
A bigger Qualcomm push into RISC-V could hurt Arm’s revenues given Qualcomm’s size, but RISC-V still faces major software and ecosystem hurdles before it can fully replace Arm for high-performance workloads.

⚠️ L'affaire Anthropic: When government bullies the builders

Faster, Please! • 1188 implied HN points • 02 Mar 26

🕹 Technology AI Regulation National Security Policy Business Innovation Ethics

Governments have a legitimate final say on national security, but that can clash with companies that want clear, predictable rules to operate by.
Branding an AI firm a security risk for limiting military use risks undermining trust and could scare off investment and innovation.
Democracies must balance security powers with protections against arbitrary government coercion, or economic growth and technological progress suffer.

Anthropic Doesn’t Decide Our Military’s Rules of Engagement

Common Sense with Bari Weiss • 273 implied HN points • 12 Mar 26

🕹 Technology AI Defense Tech National Security Tech Policy AI Ethics Military

Private AI companies shouldn't try to set the terms for how the military uses their tech; decisions about rules of engagement belong to the armed forces and government.
When a company tried to control military use, it sparked a public clash and led to the company being sidelined, which can limit timely access to important defense tools.
Tech firms should focus on protecting soldiers by building reliable, safe systems and cooperating with the Pentagon instead of fighting it over usage terms.

The Sequence Knowledge #825: Inside World Labs Marble

TheSequence • 259 implied HN points • 17 Mar 26

🕹 Technology AI Machine Learning Computer Vision 3D Modeling Simulation Generative AI Deep Learning

Marble shifts focus from predicting video frames to building spatial intelligence instead of just generating pixels.
It’s a Large World Model that reconstructs, generates, and simulates persistent 3D environments for richer, longer-lived scene understanding.
The core idea is lifting 2D inputs into a 4D representation (adding depth and time) so the model can build and reason about persistent 3D worlds over time.

Alexa, Stop Recording Me

Common Sense with Bari Weiss • 333 implied HN points • 10 Mar 26

🕹 Technology Privacy Security Surveillance Consumer Tech Hardware IOT Startups

A new consumer device called Spectre I claims to stop unwanted audio recordings by nearby smart recorders, pitched as a sleek anti-surveillance dome.
A short social media video about the device went viral and generated strong public interest and excitement.
Many people are skeptical about its effectiveness and safety, with some fearing it could be a Trojan horse for surveillance or otherwise be misused.

My thoughts on AI safety

Noahpinion • 13353 implied HN points • 15 Dec 25

🕹 Technology Artificial Intelligence AI Governance AI Ethics Existential Risk Tech regulation Risk mitigation

A superintelligent AI could conceivably pose an existential risk, but what it would want or do is largely unknowable.
Trying to prevent every possible risk by banning or imprisoning researchers would likely stall important technological progress and is probably a bad way to live.
Many other technologies and social changes also carry catastrophic risks, so we should favor cautious, practical risk reduction over total avoidance and pay attention to the realistic dangers we face now.

You gotta think outside the hypercube

lcamtuf’s thing • 5101 implied HN points • 26 Jan 26

🕹 Technology Computer Graphics Visualization Geometry Mathematics Rendering Algorithms

You can build a tesseract wireframe by extending the same edge-construction rules from a square to a cube and then to 4D, which yields 16 vertices and 32 edges.
Rotations in four dimensions are still planar operations that act on pairs of axes, so animations come from applying familiar 2D rotation formulas to axis pairs like XZ or Z🌀.
There are many ways to project 4D to 2D with different tradeoffs—cavalier, cabinet, isometric, perspective, and fisheye—and a mixed approach (isometric for XYZ plus perspective or fisheye for the fourth axis) gives the clearest, most informative views.

Go crazy, folks, go crazy

benn.substack • 1636 implied HN points • 13 Feb 26

🕹 Technology AI Software Product Startups Automation Ethics Analytics

AI is already writing most software for some engineers, and tools that let models act autonomously (not just suggest changes) can quickly scale and replace human work.
Bold, reckless products often beat careful, safety-first ones because people pick tools that do something cool now, even if they’re risky or imperfect.
Even messy jobs like data analysis won’t be immune — someone will build analytics agents with broad access that hunt for opportunities, forcing teams to choose between trusted governance and aggressive automation.

“Scale Is All You Need” is dead

Marcus on AI • 14307 implied HN points • 08 Dec 25

🕹 Technology Artificial Intelligence Machine Learning AI Research AI Policy AI Ethics Industry Impact Cognitive Science

The belief that just scaling up models and data will by itself produce general intelligence has failed and the community is finally recognizing its limits.
Current generative models are still unreliable — they hallucinate, struggle with reasoning and facts, and many businesses aren’t seeing the promised ROI.
The next phase should be interdisciplinary: borrow ideas from cognitive science and combine symbolic, causal, and world-model approaches to build more reliable, human-informed AI.

Reading List 01/03/2026

Construction Physics • 7516 implied HN points • 03 Jan 26

🕹 Technology Construction Infrastructure AI Materials Urbanism Sustainability

Large language models are opening a new path for automated building code checks by reading construction documents, and startups claim big accuracy and time savings, but the construction industry’s risk aversion and imperfect AI accuracy remain barriers.
Meranti (lauan) plywood is widely used for RV interiors and other lightweight construction, and heavy U.S. demand may be driving deforestation in Southeast Asia with serious ecological and social consequences.
Big policy and planning interventions—like the old national raisin reserve to control supply and the creation of Nusantara as a new capital—show how governments sometimes reshape markets or build cities to address economic and environmental problems.

AI Cartoon of The Year — and five (re)readings for 2026

Marcus on AI • 9169 implied HN points • 30 Dec 25

🕹 Technology Artificial Intelligence Machine Learning Generative AI Language Models AI Research

A sharp cartoon captured and critiqued the hype around AI, showing how popular narratives can run ahead of what the technology actually delivers.
Recent essays stress that LLMs still hallucinate, struggle with true generalization, and operate very differently from human reasoning, exposing key technical limits.
Because of those limits, the field is likely to shift from pure LLMs toward systems with explicit world models and neurosymbolic methods, and those newer approaches may overtake current models over time.

45 Thoughts About Agents

Am I Stronger Yet? • 846 implied HN points • 02 Mar 26

🕹 Technology Artificial Intelligence Software Development Automation Productivity AI safety Compute Infrastructure Human factors

AI agents are the fastest-moving layer of the AI stack and are accelerating capabilities through rapid software updates and user-driven experimentation. They make ambitious tasks feasible and are already changing what people can build and how quickly.
Getting real value from agents means reshaping workflows: pick agent-shaped tasks, give very clear success criteria, and have agents check their own work or use separate checkers to avoid endless revision loops. Good prompts and orchestration often save far more time than fixing sloppy outputs.
Widespread agent use will create big productivity gains and new kinds of risk at the same time — think compute limits, safety tradeoffs, and the possibility of autonomous or rogue agents — so adoption will bring fast cultural change and new policy questions.

AWS Trainium3 Deep Dive | A Potential Challenger Approaching

SemiAnalysis • 12829 implied HN points • 04 Dec 25

🕹 Technology AI hardware Cloud Computing Data Centers Machine Learning

Amazon's Trainium3 chips are designed to be cost-effective and speedy, focusing on giving customers the best value. Their approach looks at everything from the hardware to the supply chain to make sure they stay competitive.
AWS is working hard to make their software more accessible for developers, especially by open-sourcing critical parts of their software stack. This move aims to create a larger community of developers who can contribute and support the Trainium ecosystem.
Trainium3 also features advanced networking capabilities that allow for smoother communication across chips, which is important for training large AI models efficiently. This positions Amazon to better compete with other tech giants in the rapidly evolving AI space.

The (possibly) coming AI backlash and information warfare

Marcus on AI • 7469 implied HN points • 06 Jan 26

🕹 Technology Artificial Intelligence Information Warfare Disinformation Cybersecurity Tech Policy Ethics

The AI boom could unravel next year as costs, weak economics, and poor regulation make big AI projects look unprofitable and prompt political and industry backtracking.
Generative AI is exceptionally good at patient, amoral mimicry, making it a powerful tool for producing mis- and disinformation at scale.
That surge in synthetic misinformation will erode public trust and create a fog of war where false pretexts can start or escalate conflicts and sow widespread chaos.

Last Week in AI #292 - Meta's AI Artifacts, Perplexity's billions, xAI API launch

Last Week in AI • 238 implied HN points • 22 Oct 24

🕹 Technology AI Machine Learning Research Startups Innovation

Meta's AI research team released eight new tools and models to help advance AI technology. This includes new language models and tools for faster processing.
Perplexity AI is seeking a $9 billion valuation as it continues to grow in the AI search market, despite facing some plagiarism accusations from major media outlets.
Elon Musk's AI startup, xAI, launched an API for its generative AI model Grok, allowing developers to connect it with external tools like databases and search engines.

TSMC Overseas Fabs – A Success?

SemiAnalysis • 13334 implied HN points • 01 Dec 25

🕹 Technology Semiconductors Manufacturing Economics Supply Chain Innovation

TSMC is a key player in semiconductor manufacturing, but most of its production happens in Taiwan. Their overseas expansions to the U.S., Japan, and Germany face challenges in replicating the efficiency and ecosystem found in Taiwan.
The founder, Morris Chang, is skeptical about the success of U.S. fabs, suggesting that high costs and a lack of local supply chains could make them less competitive compared to TSMC's operations in Taiwan.
The U.S. government is pushing for onshore semiconductor production for national security reasons, but building and operating fabs in places like Arizona is complicated and significantly more expensive than in Taiwan.

Weekly Top Picks #116

The Algorithmic Bridge • 297 implied HN points • 13 Mar 26

🕹 Technology AI Cybersecurity Product Labor Ethics Geopolitics Culture

The AI race is consolidating around a few frontier labs — ChatGPT, Claude, and Gemini — while challengers like xAI/Grok and Meta are losing talent or delaying flagship models.
Safety, ethics, and trust are in crisis: AI tools have been linked to harmful targeting decisions, major corporate AI platforms were breached quickly, and public polls show strong dislike of AI.
AI’s real impact on work is about making jobs irrelevant, not just automating tasks, and people’s mixed reactions (like preferring AI writing) reflect a tension between perceived value and belief.

13 thoughts on Anthropic, OpenAI and the Department of War

Silver Bulletin • 935 implied HN points • 28 Feb 26

🕹 Technology AI National Security Regulation Tech industry Ethics Government Policy

AI hit an inflection point in early 2026 and is now a central political and economic issue that forces high-stakes, real-world decisions.
Government actions around Anthropic and the Pentagon’s deal with OpenAI show how politics can reshape competition, steer which models get used, and cause talent and reputational shifts in the industry.
AI capabilities appear to have stepped up recently, making rapid deployment and governance urgent and heightening concerns about safety, democratic oversight, and long-term risk.

Claude Codes

Don't Worry About the Vase • 7302 implied HN points • 09 Jan 26

🕹 Technology AI Programming Developer Tools Automation Productivity Software Engineering

Claude Code with Opus 4.5 feels like a mini-you: it can write code, control your browser and desktop, and run background automations that massively speed up building and personal workflows.
The real wins come from setup and skill — using skills, plugins, MCPs, Chrome integration, permission rules, and verification hooks makes Claude Code reliable and repeatable, and rescuing important context into files avoids token/compaction problems.
Be cautious about hype: it’s very powerful but still makes mistakes, can be untrustworthy on precise or novel tasks, and some uses (or elaborate PKM work) may waste time without expert oversight.