Agentic Coding is a Trap

Interactive systems are tools that help users achieve their goals.

The remarkable efficiency, flexibility, and scalability of computers as tools boil down to the concept of a programmable machine capable of interpreting computer programs.

Programmability lends computers their power as tools.

A key technical construct in HCI is the user interface. It refers to the parts of an interactive system that the user comes into contact with or that in other ways shape the user's perception of the system.

In HCI, evaluation refers to the application of some systematic methodology to attribute human-related values to an artifact, prototype, system, or process.

A special part of a computing system is the user interface. It is the part that the user can see and utilize to control the computer.

Programmability lends computers their power as tools.

It is an egocentric fallacy to assume that others are like us—to attempt to explain other people by reference to one's own experience.

The 2026 Global Intelligence Crisis

What 4 engineers with 10+ years of experience say about staying relevant in the AI era

Ask HN: What skills are future proof in an AI driven job market?

If most efficiency improvements came from a small handful of scale-dependent innovations, then existing models of the software intelligence explosion may be flawed.

On RE-Bench's five open-ended extension tasks, preserved failure traces in ARA accelerate progress, but can also constrain a capable agent from stepping outside the prior-run box depending on the agent's capabilities.

Tolerable for human readers, these costs become critical when AI agents must understand, reproduce, and extend published work.

We also learned that treating agents as rigid nodes in a state machine doesn't work well. Models get smarter and can solve bigger problems than the box we try to fit them in.

Our early versions of agentic work was only asking Codex to implement the task. That approach proved too limiting. Codex is perfectly capable of creating multiple PRs as well as reading review feedback and addressing it.

When our engineers no longer spend time supervising Codex sessions, the economics of code changes completely. The perceived cost of each change drops because we're no longer investing human effort in driving the implementation itself.

Among some teams at OpenAI, we saw the number of landed PRs increase by 500% in the first three weeks.

Six months ago, while working on an internal productivity tool, our team made a controversial (at the time) decision: we'd build our repo with no human-written code. Every line in our project repository had to be generated by Codex.

Instead of using domain knowledge to prescribe team organization, roles, or workflows, Fugu learns to dynamically assemble agents from a pool and coordinate them through non-obvious but highly efficient collaboration patterns.

He argues that specific algorithmic “cleverness” matters far less than the massive scaling of a few fundamental inputs

we are nearing the “end of the exponential” for AI development

The good world is where everyone has AI, and not as a revokable privilege through an API, but through hard possession.

In one case [first reported by the Financial Times](https://www.ft.com/content/00c282de-ed14-4acd-a948-bc8d6bdb339d?syn-25a6b1a6=1), an Amazon Web Service agent called Kiro purportedly decided the best way to upgrade a particular software service was to delete the whole thing and start over — and was able to do so without asking for human permission

Instead of just answering a user’s questions, the way a chatbot does, agents can take a human user’s instructions and act on them

We’ve seen remarkable adoption since its launch, with over 103,000 agents built and a total of more than 1.1 million agent sessions recorded

Military personnel and Defense Department civilians have used a version of Google Gemini’s [Agent Designer](https://docs.cloud.google.com/gemini/enterprise/docs/agent-designer) to create over 100,000 semi-autonomous AI agents in less than five weeks since the tool became available

We built AI into our editor's foundation instead of bolting it on top.

But there’s a critical difference between using agents to accomplish defined objectives and spinning up 20 agents because the dashboard makes you feel like a general commanding an army.

The average employee AI usage was 1.5 hours per week. The average CEO AI usage was less than one hour per week.

The enthusiasm has spawned an entire ecosystem of tools designed to make you feel like you’re running a company with AI agents.

37,000 lines per day. And this was the output.

Two prominent tech leaders, both publicly using the word psychosis. Both framing sleeplessness and obsessive agent usage as a feature of the moment rather than a bug.

It’s feeling like a new form of [AI psychosis](https://en.wikipedia.org/wiki/Chatbot_psychosis).

Anthropic says it has no way to control or shut down its AI models once they're deployed by the Pentagon

Bun operates its own fork of Zig, and recently achieved a 4x performance improvement on Bun compile after adding 'parallel semantic analysis and multiple codegen units to the llvm backend'.

Putting a leaderboard in place was always going to incentivize much more AI usage.

The rankings, set up by a Meta employee on its intranet using company data, measure how many tokens — the units of data processed by AI models — employees are burning through.

The rankings, set up by a Meta employee on its intranet using company data, measure how many tokens — the units of data processed by AI models — employees are burning through.

Employees at Meta Platforms who want to show off their AI superuser chops are competing on an internal leaderboard for status as a “Session Immortal”— or, even better, “Token Legend.”

Today’s LS guest, Mikhail Parakhin, CTO of Shopify, had another take on the “tasteful tokenmaxxing” - you want to go for depth (e.g. do more serial autoresearch loops) than go for breadth (e.g. solve a problem by kicking off 5, 10, 50, 500 parallel runs of the LLM slot machine). Worth thinking through.

the top conversations we have been hearing from AI leadership (CTOs, VPs, Founders) have all centered around the concept of “Tokenmaxxing” and how leaders want to get their teams using more AI, WITHOUT the downside of incentivizing the kinds of horrendous waste

AI News for 4/21/2026-4/22/2026. We checked 12 subreddits, [544 Twitters](https://twitter.com/i/lists/1585430245762441216) and no further Discords.

Endorsement reversal occurred in fewer than 3 in 1,000 observations.

AI systems currently provide more consistent fraud warnings than lay humans in an identical advisory role.

The AI-generated image of Neukgu had prompted Daejeon city government to issue an emergency text to residents, warning them of a wolf near the intersection.

The most urgent finding this week comes from researchers who demonstrated that the very mechanism enabling agents to use tools - function calling - can be hijacked with alarming reliability.

Nothing in between. A model that arrives at the correct answer through careful reasoning receives the same reward as one that guesses correctly by chance.

They deliver every answer with the same unshakable certainty, whether they're right or guessing.

And it’s not just the US putting chatbots at commanders’ fingertips; China is commissioning similar tools, according to recent [analysis] by Georgetown University’s Center for Security and Emerging Technology.

Today’s military personnel might give chatbots a list of potential targets to help decide which to strike first.

The transition from isolated AI models to the aggregated, metered token economy will unlock the twenty-first.

Consider the deep anatomy of an individual AI session to understand how this telemetry actually works in practice.

The smartest companies are no longer just hiring talent; they are purchasing synthetic intelligence by the gigawatt.

The smartest companies are no longer just hiring talent; they are purchasing synthetic intelligence by the gigawatt.

We do not publish AI-generated images, audio, or video as authentic documentation of real events.

Anyone who uses AI tools in our editorial workflow is responsible for the accuracy and integrity of the resulting work.

These standards have governed our editorial work since AI tooling became available.

We don’t publish claims based solely on AI-generated summaries, and reporters may not represent any material as “reviewed” unless they have examined it directly.

Ars Technica is written by humans. Our reporting, analysis, and commentary are human-authored.

We do not publish AI-generated images, audio, or video as authentic documentation of real events

Reporters may use AI tools vetted and approved for our workflow to assist with research

Our reporting, analysis, and commentary are human-authored

AI cannot replace human insight, creativity, and ingenuity

The company has also had to make major investments in its AI efforts in order to keep up with competitors in the space — earlier this month, it debuted a completely overhauled AI product called Muse Spark.

In truth, nate relied heavily on teams of human workers—primarily located overseas—to manually process transactions in secret, mimicking what users believed was being done by automation

Authoritarianism in all its forms depends on people acting against their own interests, in this case seeing the abdication of agency and giving up of rights as something positive, as almost a relief. Let the leader decide for you! Let the leader, who is an agent of divine providence, decide your destiny! Getting people to believe this is one of the main functions of personality cults that depict the leader as infallible.

The LLM took an entirely different route, using a formula that was well known in related parts of math, but which no one had thought to apply to this type of question.

They range dramatically in both significance and difficulty, and many AI solutions have turned out to be less original than they appeared.

The LLM took an entirely different route, using a formula that was well known in related parts of math, but which no one had thought to apply to this type of question.

But experts have warned that these problems are an imperfect benchmark of artificial intelligence's mathematical prowess. They range dramatically in both significance and difficulty, and many AI solutions have turned out to be less original than they appeared.

We have discovered a new way to think about large numbers and their anatomy. It's a nice achievement. I think the jury is still out on the long-term significance.

Liam Price just cracked a 60-year-old problem that world-class mathematicians have tried and failed to solve. He's 23 years old and has no advanced mathematics training.

Liam Price just cracked a 60-year-old problem that world-class mathematicians have tried and failed to solve. He's 23 years old and has no advanced mathematics training.

I had the intuition that these problems were kind of clustered together and they had some kind of unifying feel to them. And this new method is really confirming that intuition.

The LLM took an entirely different route, using a formula that was well known in related parts of math, but which no one had thought to apply to this type of question.

Resolution increases make them more expensive, then efficiency gains reduce costs - a sawtooth pattern.

Will smarter models be increasingly expensive because of greater accuracy or less expensive because they're smarter?

Smaller pieces force the model to pay closer attention to each word, like reading a contract word by word instead of skimming paragraphs.

Then Opus 4.7 shipped & the smarter model became much more expensive. The cause : a new tokenizer

Opus 4.5 costs 67% more than Sonnet. But Opus 4.5 used 76% fewer tokens to reach the same outcome.

When Anthropic launched Opus 4.5 in November 2025, the bigger, more expensive model was actually cheaper to use.

The agent interprets new information and adapts the logic. The engine applies that logic continuously and emits precise updates.

Agents and CDC streams are powerful together because they split the work well.

The fix is not smarter prompts. It is software built to meet agents halfway.

Today's agents, the copilots, the chatbots are designed to be human like.

The agent interprets new information and adapts the logic. The engine applies that logic continuously and emits precise updates.

Agents and CDC streams are powerful together because they split the work well.

With change data capture (CDC), the system emits a stream of precise updates: inserts, updates, deletes, each tied to specific records.

The fix is not smarter prompts. It is software built to meet agents halfway.

Today's agents, the copilots, the chatbots are designed to be human like.

We have always been wary of AI generated code, but felt everyone is free to do what they want and experiment, etc.

This ultimately also leads to false positives, but my manual QA run verified it's maybe 5-10%.

LLM tend to use certain font combos like Space Grotesk, Instrument Serif and Geist

Claude Code has led to a large increase in Show HN projects. So much, that the moderators of HN had to restrict Show HN submissions for new accounts.

I guess people will get back to crafting beautiful designs to stand out from the slop. On the other hand, I'm not sure how much design will still matter once AI agents are the primary users of the web.

Is this bad? Not really, just uninspired. After all, validating a business idea was never about fancy design, and before the AI era, everything looked like Bootstrap.

A designer recently told me that 'colored left borders are almost as reliable a sign of AI-generated design as em-dashes for text'

The good world is where everyone has AI, and not as a revokable privilege through an API, but through hard possession.

It works for Mars. I think there's so much value in colonizing Mars, and it's sad to me to see SpaceX diluting the mission buying up random AI bubble crap.

Even the ideal version, industrial megaprojects at hyperhuman scale while constantly being out over your skis with leverage sounds hellish.

Three of the four metrics (ECI, log METR 50% time horizon, and a math-focused index we constructed from several math benchmarks) show strong evidence that progress has sped up relative to a global linear trend fit to data from 2023 onward.

Three of the four metrics (ECI, log METR 50% time horizon, and a math-focused index we constructed from several math benchmarks) show strong evidence that progress has sped up relative to a global linear trend fit to data from 2023 onward.

Several correlated but not strictly identical changes happened over the same few months: scaling inference compute, heavier use of RL in post-training, and models producing reasoning tokens.

Tasks where correctness is harder to verify may not have seen the same speedup, so the acceleration we document here may not be as general as the headline numbers suggest.

The three metrics where we find acceleration are concentrated in programming and mathematics. These are areas that labs have explicitly targeted for improvement, and they share an important property: correctness is easy to verify automatically.

Three of four metrics show strong evidence of acceleration, seemingly driven by reasoning models.

Three of four metrics show strong evidence of acceleration, seemingly driven by reasoning models.

Three of four metrics show strong evidence of acceleration, driven by reasoning models.

Three of four metrics show strong evidence of acceleration, driven by reasoning models.

The result is a mismatch that should terrify anyone building software: the attack surface is expanding faster than any human can monitor, and the entities making dependency decisions are increasingly not human.

Ask ten different programmers how they use AI, and you can get ten different answers.

over one million Trainium2 chips to train and serve Claude

Amazon is investing $5 billion in Anthropic today, with up to an additional $20 billion in the future. This builds on the $8 billion Amazon has previously invested.

Anthropic will also use incremental capacity for Claude in Amazon Bedrock. The agreement includes expansion of inference in Asia and Europe to better serve Claude's growing international customer base.

Our run-rate revenue has now surpassed $30 billion, up from approximately $9 billion at the end of 2025.

We have signed a new agreement with Amazon that will deepen our existing partnership and secure up to 5 gigawatts (GW) of capacity for training and deploying Claude

up to 5 gigawatts (GW) of capacity for training and deploying Claude

over one million Trainium2 chips to train and serve Claude

up to 5 gigawatts (GW) of capacity for training and deploying Claude

Testing universal jailbreaks for biorisks in GPT‑5.5

As part of this investigation, we ran more ablations (removing lines from the system prompt to understand the impact of each line) using a broader set of evaluations. One of these evaluations showed a 3% drop for both Opus 4.6 and 4.7.

We reverted this change on April 7 after users told us they'd prefer to default to higher intelligence and opt into lower effort for simple tasks.

Anthropic made fun of this idea during the last Super Bowl.

The network requirement is only for the initial download of the model. Subsequent use of the model does not require a network connection. No data is sent to Google or any third party when using the model.

I would put venture capitalist in finite demand & open loop. There's only a certain amount of venture capital dollars entering the ecosystem in a year, & investment selection remains an open problem.

Open Loop + Finite Demand = Utility Tools. Preparing 10-Ks & 10-Qs. Legal contract review. Insurance claims processing. One report per quarter, one contract per deal. AI makes the work faster, but doesn't create new work to do.

Closed Loop + Infinite Demand = Economic Engines. Software engineering lives here. AI writes the code. Tests verify correctness. More code enables more features. Companies will always need more software.

The compliance-driven buyers improvising local AI out of retail Mac Minis because the product they need does not exist.

Why the company that moved computing off the mainframe fifty years ago is making the same structural move with AI, and what that predicts.

The question it forces is not which model is best. It is who owns the inference layer your organization depends on, what happens when the economics of that layer stop being subsidized, and whether the thing in your pocket turns out to matter more than the thing in the datacenter.

The structural cost problem in AI inference that makes Apple's on-device bet defensible, not just defensive.

This means that improvements on SWE-bench Verified no longer reflect meaningful improvements in models' real-world software development abilities. Instead, they increasingly reflect how much the model was exposed to the benchmark at training time.

Our RL infra team used a K2.6-backed agent that operated autonomously for 5 days, managing monitoring, incident response, and system operations, demonstrating persistent context, multi-threaded task handling, and full-cycle execution from alert to resolution.

The architecture scales horizontally to 300 sub-agents executing across 4,000 coordinated steps simultaneously, a substantial expansion from K2.5's 100 sub-agents and 1,500 steps.

Kimi K2.6 autonomously overhauled exchange-core, an 8-year-old open-source financial matching engine. Over a 13-hour execution, the model iterated through 12 optimization strategies, initiating over 1,000 tool calls to precisely modify more than 4,000 lines of code.

Kimi K2.6 demonstrates significant improvements over Kimi K2.5 in internal evaluations conducted by CodeBuddy: code generation accuracy increased by 12%, long-context stability improved by 18%, and tool invocation success rate reached 96.60%.

Meta founder and CEO Mark Zuckerberg described superintelligence in a blog post last year

Claude is now being deployed to NEC Group employees around the world

NEC will establish a Center of Excellence to develop a highly skilled, AI-enabled engineering organization

As part of its long-running Client Zero initiative, in which NEC serves as its own first customer before offering its technology to clients

NEC aims to build one of Japan's largest AI-native engineering teams, who will use Claude Code in their work.

Our most complex pages, which took 20+ prompts to recreate in other tools, only required 2 prompts in Claude Design.

Claude Design gives designers room to explore widely and everyone else a way to produce visual work.

GPT‑5.5 found a proof of a longstanding asymptotic fact about off-diagonal Ramsey numbers, later verified in Lean. The result is a concrete example of GPT‑5.5 contributing not just code or explanation, but a surprising and useful mathematical argument in a core research area.

We are treating the biological/chemical and cybersecurity capabilities of GPT‑5.5 as High under our Preparedness Framework. While GPT‑5.5 didn't reach Critical cybersecurity capability level, our evaluations and testing showed that its cybersecurity capabilities are a step up compared to GPT‑5.4.

Losing access to GPT‑5.5 feels like I've had a limb amputated.

GPT‑5.5 delivers this step up in intelligence without compromising on speed: larger, more capable models are often slower to serve, but GPT‑5.5 matches GPT‑5.4 per-token latency in real-world serving, while performing at a much higher level of intelligence.

GPT‑5.5 found a proof of a longstanding asymptotic fact about off-diagonal Ramsey numbers, later verified in Lean. The result is a concrete example of GPT‑5.5 contributing not just code or explanation, but a surprising and useful mathematical argument in a core research area.

The viable path is trusted access, robust safeguards that scale with capability, and the operational capacity to detect and respond to serious misuse.

We are treating the biological/chemical and cybersecurity capabilities of GPT‑5.5 as High under our Preparedness Framework. While GPT‑5.5 didn't reach Critical cybersecurity capability level, our evaluations and testing showed that its cybersecurity capabilities are a step up compared to GPT‑5.4.

GPT‑5.5 delivers this step up in intelligence without compromising on speed: larger, more capable models are often slower to serve, but GPT‑5.5 matches GPT‑5.4 per-token latency in real-world serving, while performing at a much higher level of intelligence.

The gains are especially strong in agentic coding, computer use, knowledge work, and early scientific research—areas where progress depends on reasoning across context and taking action over time.

We are treating the biological/chemical and cybersecurity capabilities of GPT‑5.5 as High under our Preparedness Framework. While GPT‑5.5 didn't reach Critical cybersecurity capability level, our evaluations and testing showed that its cybersecurity capabilities are a step up compared to GPT‑5.4.

GPT‑5.5 is priced higher than GPT‑5.4, it is both more intelligent and much more token efficient. In Codex, we have carefully tuned the experience so GPT‑5.5 delivers better results with fewer tokens than GPT‑5.4 for most users

The viable path is trusted access, robust safeguards that scale with capability, and the operational capacity to detect and respond to serious misuse.

OpenAI pledged $1.5B to a joint venture called DeployCo, guaranteeing private-equity partners a 17% annual return floor over five years.

Jeremy didn't get laid off. He got leveraged.

The Meta cuts are the inverse. When one person with the right AI tools can do the work of 10-to-15 people, the person most at risk isn't the one using the AI. It's the one whose job description overlaps with what AI now does by itself.

A US lab would never; well, unless you count a code red or Meta's throw money at the problem moves.

Our alignment assessment concluded that the model is 'largely well-aligned and trustworthy, though not fully ideal in its behavior'. Note that Mythos Preview remains the best-aligned model we've trained according to our evaluations.

On some measures, such as honesty and resistance to malicious 'prompt injection' attacks, Opus 4.7 is an improvement on Opus 4.6; in others (such as its tendency to give overly detailed harm-reduction advice on controlled substances), Opus 4.7 is modestly weaker.

Opus 4.7 is better at using file system-based memory. It remembers important notes across long, multi-session work, and uses them to move on to new tasks that, as a result, need less up-front context.

Interestingly, this means that prompts written for earlier models can sometimes now produce unexpected results: where previous models interpreted instructions loosely or skipped parts entirely, Opus 4.7 takes the instructions literally.

For Anthropic, more usage across diverse tasks means more data, which produces a smarter model—just as more queries improved Google search.

A free, good-enough product is enough to change market dynamics.

The risk of this strategy to the ecosystem is that it makes previously attractive categories no longer viable.

The commoditization flywheel : both companies give away complements to drive usage of the core.

For Anthropic, more usage across diverse tasks means more data, which produces a smarter model—just as more queries improved Google search.

I'm not going to trust them to measure it.

If 90% is AI, do we even need a team?

Writing code is not the same as software development. This is only capturing some level of acceleration while writing code, and does not capture time taken in architecture, debugging, review, and deployment.

Cursor counted the entire file as AI, even though we can see from the diff that it left plenty of the lines unchanged.

So even though I did 100% of the writing and 50% of the refactoring, Windsurf reports that 100% of the code I produced in that session was generated by AI.

customers should expect PCW values of 85%+, often 95%+. This is not a hallucination and is accurate given how we compute this metric

Security is a defensive posture; agency is a functional right.

placing constraints upon them not only helps users and services build trust in them, but it also helps people more easily conceptualise what they do.

Some proposals for AI agents assume that putting agentic code in a TEE or similar 'jail' will solve these problems, but that ignores the need to collectively bargain

lack of a well-defined user agent role in AI that's backed up by transparent, public standards... leaves a gap – it makes it harder for a marketplace to form.

A LeadDev survey found 54% of engineering leaders believe AI copilots will reduce junior hiring long-term.

When juniors skip debugging and skip the formative mistakes, they don't build the tacit expertise. And when my generation of engineers retires, that knowledge doesn't transfer to the AI.

I have baked Karl Popper in the main company AI skill: everything we create (human or AI) should be challenged.

"It does not save time or offer anything of value if every single line needs to be double-checked and re-translated and it reduces the optics of their job to that of 'text janitor.' Real translators have been kicked so hard by AI that you should not blame them for not picking up the sloppy seconds of a chatGPT translation patch. They deserve better."

This is the first part I reject. The moving things around is precisely what thinking and writing involves. It's where ideas are born and cultivated, shaped to become what we have in mind. The rearranging of words to capture an incipient thought is the struggle and joy of being a writer.

When a Fugu model is allowed to call itself recursively, reading its own prior output as context and deciding whether to revise its coordination strategy, a new form of test-time scaling emerges.

A core conviction at Sakana AI is that the most capable AI systems will not be monolithic models scaled in isolation, but collections of specialized agents working together.

DeepSeek does not appear to have fully moved beyond Nvidia. The company's technical report reveals that it is using Chinese chips to run the model for inference, but...appears to have adapted only part of V4's training process for Chinese chips.

In a 1-million-token context, V4-Pro uses only 27% of the computing power required by its previous model, V3.2, while cutting memory use to 10%.

DeepSeek V4 exceeds them all on coding, math, and STEM problems, making it one of the strongest open-source models ever released.