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MIT Technology Review Explains: Let our writers untangle the complex, messy world of science and technology to help you understand what’s coming next. You can read more from the series here. Your brain lives in the dark space of your skull. Yet it knows when the wind lifts the hairs on your skin, when your heart is racing, when your gut tightens with fear. It’s also, right now, predicting what you’ll read next as your eyes move across this page. It’s picking up signals that help it make sense of what’s happening around you and prepare you to act if you need to stay safe. You aren’t usually aware that your brain is doing all that. Our senses take in information at a staggering rate—roughly 11 million bits flood in every second from our skin, eyes, ears, and more. That’s nearly three paperback novels’ worth of data every second. Only a sliver reaches our conscious awareness.  Researchers estimate that our conscious minds can process roughly 10 to 60 bits of information per second, about the rate at which you’re reading this sentence. That’s a ratio of about one conscious bit to hundreds of thousands of unconscious bits. And that’s a mercy. As Moriah Thomason, a neuroscientist at NYU Langone, says, “Thank goodness we’re built like this. There’s a layer of what we have access to in conscious awareness. And then we have a right-under-the-surface amount. There is only a certain amount we are meant to ‘hold in mind’ in order to function successfully.”  What you are aware of: Your stomach growling when you’re hungry. Your palms sweating before you speak in public. The breath you just took, if you pay attention to it. Even your heartbeat, which some people can sense from the inside without feeling their pulse in their wrist. Scientists have a word for how we sense ourselves from the inside: interoception.  The term was coined in 1906 by the British neurophysiologist Charles Sherrington. For most of the 20th century it remained largely confined to textbooks. Today, thanks to a 2021 Nobel Prize and new tools that can map the interoceptive system across the body, the study of this facility is suddenly quite hot. As researchers decode how signals move between body and brain, a clearer picture is starting to take shape—with implications for how we understand and treat conditions from obesity to chronic pain to anxiety. The field began to take off in the 1990s. In 1994, the neurologist Antonio Damasio published a book with a pointed title: Descartes’ Error. He challenged the historical separation of thinking and feeling, arguing that our ability to choose and act is driven by feelings, and those feelings in turn are shaped by the body’s signals, such as your gut clenching or your skin going clammy. When we lose that connection between feeling and thinking, as one of Damasio’s patients did after surgery to treat a brain tumor, we may still be able to reason with perfect logic about the pros and cons of traveling on a Tuesday or a Wednesday. But without the emotional signals that help us predict what a choice will feel like, our reason spins and circles, and we cannot decide. A contemporary of Damasio’s, the neuroscientist Bud Craig, spent his career asking one question: How do you feel? He charted how the brain builds an inner map of the body and updates it in real time every moment you are alive. Think of the captain’s bridge on the USS Enterprise, where a live map displays the status of the ship’s critical systems: oxygen levels, energy availability, hull integrity, shield strength. Another set of indicators senses things outside the ship: asteroid belts, enemy ships, radiation, life signs, and spatial anomalies not yet understood. Your brain, only about the size of your two fists pressed together, creates a map like this for your entire body, along with a map of the outside world, from data streaming in through your five senses. Together, they feed into your brain’s working model of you in the world, now and across time—where you are, who you are, your expectations for what’s about to happen (based on everything you know), and what all that means for you. When someone asks “How are you doing?” we consult our maps and report back on our status. We might say we’re happy, depleted, anxious, or energetic. These feelings are always a braid of emotional and physical sensations. They’re what your interoceptive navigational system serves up to your awareness when you sense yourself from the inside. As we grow up, we learn to interpret what these sensations mean—interpretations that, in turn, can alter our physiology, emotions, and behavior. Research by the psychologist Alia Crum shows that people who embrace a “stress is enhancing” mindset produce more growth hormones than people who have a “stress is debilitating” mindset. They also experience more positive emotions and greater cognitive flexibility. Language also matters. We learn words for the textures of our feelings—words that then shape how we feel and act. People low in emotional “granularity”—as the psychologist Marc Brackett calls the ability to distinguish between closely related feelings—react more impulsively under stress and are less able to find meaning in difficult experiences. But mindsets and emotional intelligence are malleable. We can learn that “anxious” is different from “terrified,” and we can even reframe how we interpret our body’s sensations. Instead of thinking of the butterflies in our bellies as annoying, we can welcome them as our body’s way of preparing us for a peak performance. Scientists have long understood that the interoceptive information informing these lived experiences travels via two major systems: nerves and humors (blood and lymph). Now they’re actively studying a third system—the “interstitium,” a network of fluid-filled spaces woven throughout the body’s connective fascia that may also play a role in communication. But until recently, scientific understanding of this interoceptive system looked like a high-level schematic that left out vital details—how information travels from the outside environment in,

It’s a tale of two nuclear industries. In China, large reactors are coming together at a stunning pace. The country has nearly doubled its nuclear fleet since 2016, reaching nearly 60 gigawatts of total power capacity. The new facilities are nearly all gigawatt-scale pressurized-water reactors. Meanwhile, the US has built just two reactors in that time—Unit 3 and Unit 4 at Plant Vogtle in Georgia. Smaller reactors are attracting a lot of excitement and investment, though. A microreactor developer just saw its reactor reach criticality in a new Department of Energy pilot program. The world is racing to meet rising electricity demand, and many countries are interested in energy sources, like nuclear power, that don’t come with greenhouse-gas emissions. The key question: Which of these strategies will really pay off in terms of getting electrons on the grid quickly?   Today, the US and France are known as leaders in the nuclear industry. The US has the world’s largest fleet, with France coming in second. France is heavily dependent on nuclear for its grid—about two-thirds of the country’s power comes from nuclear reactors. But they have hardly added any new reactors to their fleets in recent years. The US can point only to Vogtle, and France connected its latest reactor to the grid in December 2024—the first in over 20 years.  It’s incredibly difficult to build the massive projects that dominate the nuclear industry today. Up-front investment can run well into the billions, so investors need to wait decades to break even. Designs are complex and can often change during the regulatory process, tacking on cost and time.  Many are hoping that the key to turning things around in these countries could be smaller reactors. The idea is that shrinking the footprint of a reactor cuts down the initial investment needed to prove out the new technology. The reactors could even be put together in a factory rather than being built on-site, allowing for a lower price over time. These smaller reactors are the target of tons of interest and investment in the US, including a new Department of Energy pilot program. The department set a goal last year of having three test reactors reach criticality by July 4, 2026, the nation’s 250th anniversary. (Criticality is the point at which a reactor achieves a self-sustaining chain reaction that can release energy.) Last week, California-based Antares hit the milestone with its Mark-0 reactor.  The company plans to eventually build microreactors, designed to produce between 100 kilowatts and 1 megawatt of electricity (large reactors on the grid today are at least 1,000 times that size). The core design is a sodium-cooled reactor, and it uses TRISO fuel, self-contained graphite-coated spheres of a more concentrated fuel than what most reactors use today.  But there is still a long way to go before it can actually produce power—the Mark-0 doesn’t have any power conversion or heat removal systems. The company plans to produce electricity in late 2027 and deploy in the field by 2028, CEO Jordan Bramble told the Associated Press. The private sector is interested—and invested—too. Big Tech companies are throwing money at new reactors they hope can help power data centers.  But look to the other side of the globe, and others are sticking with the established blueprint: China is absolutely churning out large nuclear reactors. Construction started on six new reactors there in 2025, and two more got underway in the first five months of 2026. The country is on course to overtake both the US and the European Union in installed nuclear capacity by 2030. The speed here is staggering. As of 2024, the average time to build a new reactor in China came in at between five and seven years. The global average is about nine years, and the two most recent reactors in the US took about 15 years. One key to this speed is standardization: China has set up a uniform project management system to design, license, and build new reactors. They’re built in batches of six or more to take advantage of economies of scale. It’s one of the ideas meant to give the edge to smaller reactors, but China is working to realize the same benefits for larger projects. A huge amount of government investment is certainly helping. Larger reactors generally provide more electricity to the grid for a lower price, a key consideration in view of China’s steeply increasing electricity demand. While smaller reactors require less up-front investment than larger ones because of their size, they’ll actually be more expensive per unit of electricity produced.  That’s not to say China is exclusively focused on big reactors: the country is also expected to see its first operational small modular reactor, the Linglong-1, start sending power to the grid this year. But looking ahead, it’ll be interesting to see if smaller reactors can help the West keep building new nuclear power. At the moment, with China’s quick progress, it’s looking as if bigger might just be better.  This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

Imagine tuning in to the opening kickoff of a World Cup match and seeing a player intentionally send the ball all the way down the pitch and right out of bounds on the opponent’s end. Casual fans might scratch their heads. Where’s the logic in surrendering possession seconds into a game? If you were Jesse Davis, though, you’d know that this play could be a prime setup to score.  Davis is a professor of computer science at KU Leuven in Belgium and head of its Sports Analytics Lab, which has been at the vanguard of a data awakening in soccer since its inception more than a decade ago. Though the research group brings machine-­learning models to bear on a variety of sports—including basketball, volleyball, and field hockey—nowhere is its impact felt more than on the soccer pitch.  Davis and his team of researchers employ advanced data analytics to reveal a range of (beg your pardon) game-changing findings that are shifting pro clubs’ decision-making. “His lab is the most influential sports analytics lab in soccer,” says Hugo Rios-Neto, data recruitment lead for Royal Sporting Club Anderlecht in Belgium. They’ve helped teams better evaluate their rosters, conceived ways to assess how efficient (or not) strategies are, and developed algorithms that uncover hidden tactical patterns. Like, for instance, the value of kicking the ball out of bounds close to the goal and letting your opponent throw it back into play—a move that’s been popping up in some of the world’s top leagues over the last few years. To make the statistical argument for this seemingly counterproductive move, Davis’s group built a training data set composed of more than 1.4 million passes and some 60,000 throw-ins—partly from the 2022 World Cup. They used tree ensemble models (essentially a mashup of decision trees) to simulate the tactic. The conclusion, which the researchers presented in a 2024 paper under the apt title “Boot it”: When the ball is in the middle third of the pitch, kicking it out of bounds on your opponents’ side of the field can put you within 10 actions (think passes and dribbles) of a goal. That can be a big deal in a game that has 1,500 or more actions per match and very little scoring. The idea, Davis explains, is that you’re setting yourself up to recover the ball in an advantageous situation. Beyond providing discrete game-day insights, Davis also occupies a unique niche in the world of sports analytics, where many clubs now hire their own internal data teams to maintain a competitive edge. He makes most of his research freely available via open-source analytics tools, but the academic life also affords him the freedom to tackle more complex problems—like standardizing in-game data, a project that will make it easier to parse game footage and come up with winning strategies.  Davis, 45, grew up in Wisconsin and spent his childhood enraptured by basketball and (American) football. Soccer was largely a nonentity to him until college, when the 2002 World Cup—in which Brazil famously swept the tournament—reeled him in. But the notion of going on to dissect the sport never crossed his mind. His doctoral studies in computer science at the University of Wisconsin–Madison had him working with radiologists to analyze mammography reports.  In October 2010, he joined KU Leuven as a computer science professor looking at the intersection of AI and health care, with a focus on monitoring athletic performance. His research team studied, for instance, combining things like heart rate with other metrics to determine whether someone was overtraining. They also dove into the biomechanics of running. The tactical and technical aspects of sports, and soccer specifically, became the subject of Davis’s professorial work when he hired Jan Van Haaren, an engineering student focused on artificial intelligence and a self-described soccer fanatic. He wondered if data analysis could be used to study things like passing, shooting, and ball progression—metrics the game was only just beginning to digitally crunch at the time.  Davis realized that machine learning and other artificial-intelligence tools lent themselves well to the complexity, fluidity, and speed of soccer. You need not be well versed in the moneyball-ization of pro sports to see that it’s relatively easy to apply deep statistical work to baseball or basketball. You can isolate actions like jump shots and assign value to ones taken close or far away. Soon a basketball coach realizes that a player who can’t make a layup, but shoots roughly as well from the three-point line as on mid-range jumpers, might as well go for the shot that gets more points.  Soccer, by comparison, seemed like a poor candidate for that kind of analysis. “The vast, vast majority of actions really don’t lead to the outcome of a goal or even a shot,” says Rios-Neto. “So it’s hard to elaborate or derive a winning strategy from the data.” But Van Haaren’s love of the sport, and Davis’s love of sports in general, inspired them to try. Over time, Davis realized that machine learning and other artificial-intelligence tools lent themselves well to the complexity, fluidity, and speed of soccer. In 2014, he officially stood up the Sports Analytics Lab.  With a stable of about 10 students and postdocs at any one time, the lab began laying what Van Haaren calls the “intellectual foundations of how the game is analyzed today.” The researchers picked apart in-game actions, and suddenly they were valuing ball possession, penalty-kick strategy (aim for the center), and the merits of long shots on goal (take them). “One of the trends that’s been in soccer over the last five to 10 years is that the number of long shots has dramatically increased,” says Davis. “What the data let you do is really quantify what the probabilities of those things are.” In the years since Davis and his team started untangling individual soccer tactics, their ideas have started to permeate clubs across Europe, like Belgium’s Club Brugge KV, as well as national soccer organizations in the

Google DeepMind is funding research into the potential dangers of situations where millions of different AI agents interact with each other online. According to Rohin Shah, who directs the company’s AGI safety and alignment research, the mass-market arrival of agents that can carry out tasks without human oversight and follow instructions given to them by other agents creates a whole new class of risk. In an effort to address this, Google DeepMind—which made agent-based tools a centerpiece of Google I/O last month—has teamed up with several other organizations to announce a $10 million funding pot for researchers to study the behavior of multi-agent systems and come up with ways to prevent unsafe scenarios. Joining Google DeepMind are Schmidt Sciences, a philanthropic foundation set up by Eric and Wendy Schmidt; ARIA, the UK government’s moonshot agency; the Cooperative AI foundation, a UK-based nonprofit research outfit; and Google’s charitable arm, Google.org. I asked Shah and James Fox, who leads the Science of Trustworthy AI program at Schmidt Sciences, what they hope to achieve with that $10 million. It’s no small sum, but it’s dwarfed by the budgets commanded by Google DeepMind’s own research teams. The aim is to kick-start research outside tech companies, says Shah: “The strength of academia is that it can look really quite far into the future and do the kind of work that isn’t top of mind at industry labs.” “The main issue is that there just isn’t really a field of research for multi-agent safety yet,” he adds. “And we would like there to be.” The concern is that as more and more AI agents get deployed and begin working together, we could hit a tipping point where imagined scenarios become real. “We see this with humanity, too,” says Shah. “Our institutions can accomplish things that no individual human can.” Shah thinks we have a few more months to go before agents are deployed throughout the economy in numbers that make potential risks a real concern. He wants to get ahead of that moment. Risky business What risks are we talking about, exactly? The possibilities that Shah and Fox have in mind mostly boil down to supercharged versions of bad things that happen on the internet already: scams, prompt injections (where an AI agent is fed malicious instructions, turning it into a self-guiding piece of malware), other forms of cyberattack. We look at what humans do now and ask what the agent version of that would be, says Shah.   “We’ve got this digital commons that is integral to how society works, and you really want to ensure that this doesn’t descend into just absolute anarchy,” says Fox. (I asked Shah if they were considering any worst-case scenarios more on the doomer end of the spectrum, such as widespread economic collapse. “Certainly not if we’re talking by the end of the year,” he said. That’s only six months away! He laughed. “Okay, a while after that.”) Shah and Fox both think that the only way to understand what might happen when large numbers of multi-agent systems interact with each other is to run realistic simulations. They want researchers to drop AI agents into sandboxes and study what they do. You can’t predict what’s going to happen by studying single agents, or even small groups of agents, in isolation. You can’t assume that AI agents underpinned by LLMs will always act rationally, says Fox. And the complexity comes from having huge numbers of interactions at once. Some researchers, including a team at Google DeepMind, have argued that artificial general intelligence (if possible at all) could come not from a single super-smart model but from a kind of agent hive mind, where the capabilities of the whole add up to more than the sum of its parts.   Lack of trust Google DeepMind is not the only top AI firm warning about the risks of the technology it is building. A couple of weeks ago, Anthropic published guidelines for deploying AI agents based on an approach to cybersecurity known as zero trust, which starts with the assumption that a computer system is vulnerable, an agent is an attacker, and a breach will happen. Refael Angel, cofounder and CTO of Akeyless, a cybersecurity firm based in Tel Aviv, agrees that understanding the new risks introduced by agent-based systems is crucial.   Every approach to security in the past has assumed that the machine in question was software written by a human, doing fixed things on fixed paths, says Angel: “An agent breaks all of those assumptions. It reasons, it improvises, and it can be hijacked by a single sentence buried in a document it was asked to read.” Angel welcomes this new funding. “No single lab should author the safety standards everyone else has to trust,” he says. But he cautions that safety researchers can overlook boring problems that are already here in favor of more exotic hypothetical ones. And yet, Fox notes, risks that were hypothetical a few years ago are now very real: “The future’s come more quickly than perhaps expected.”

Anthropic released two models on June 9, 2026: Claude Fable 5 and Claude Mythos 5. Both belong to a tier called “Mythos-class.” This tier sits above the Opus class in capability. Fable 5 is the version claimed to be made safe for general use. Mythos 5 is the same model with some safeguards lifted, kept in limited release. Claude Fable 5 and Mythos 5 Mythos-class models are a tier of Claude models. They sit above the Opus class in capability. The first was Claude Mythos Preview, released in April through Project Glasswing. Fable 5 and Mythos 5 share the same underlying model. The difference is the safeguards. Fable 5 ships with safety classifiers for general use. Mythos 5 has some classifiers removed and stays in limited release. The names reflect this split. “Fable” comes from the Latin fabula, “that which is told.” This is akin to the Greek mythos. The safeguards distinguish the two models, so they carry different names. Anthropic team calls Fable 5 its most capable widely released model. It targets demanding reasoning and long-horizon agentic work. Anthropic states Fable 5’s capabilities exceed any model it has made generally available. Both models support a 1M token context window by default. They allow up to 128k output tokens per request. Pricing is $10 per million input tokens and $50 per million output tokens. That is less than half the price of Claude Mythos Preview. The Capability Case Anthropic reports Fable 5 is state-of-the-art on nearly all tested capability benchmarks. It shows strong results across software engineering, knowledge work, vision, and scientific research. The longer and more complex the task, the larger its lead over Anthropic’s other models. On software engineering, Stripe tested Fable 5 during early access. The model performed a codebase-wide migration in a 50-million-line Ruby codebase. According to Stripe: this took one day. By hand, a team would have needed over two months. Fable 5 is also more token-efficient than past Claude models. On Cognition’s FrontierCode evaluation, Fable 5 scores highest among frontier models. This holds even at medium effort. The eval tests difficult coding tasks under production-codebase standards. On knowledge work, Anthropic cites Hebbia’s Finance Benchmark for senior-level reasoning. Fable 5 posts the highest score of any model there. Gains come in document-based reasoning, chart and table interpretation, and problem solving. On vision, Anthropic calls Fable 5 the new state-of-the-art. It can extract precise numbers from detailed scientific figures. It can rebuild a web app’s source code from screenshots alone. It also needs less scaffolding than prior models. Fable 5 beat Pokémon FireRed with a minimal, vision-only harness. On memory and long-context, Fable 5 stays focused across millions of tokens. It improves its outputs using its own notes. In the game Slay the Spire, persistent file-based memory helped it three times more than Opus 4.8. Mythos 5 carries the science claims. Internal protein design experts accelerated parts of drug design by around ten times. Anthropic also says Mythos 5 is its first model to consistently produce novel scientific hypotheses. Scientists preferred its molecular biology hypotheses around 80% of the time in blinded comparisons. Mythos 5 also ran novel genomics research over a week of largely autonomous work. It trained a custom model on single-cell data spanning 138 animal species. Anthropic says that model outperformed a recent model published in Science, despite being 100 times smaller. How the Safeguards Work Releasing a model this capable carries risk. Without safeguards, Fable 5’s cybersecurity capabilities could be misused to cause serious damage. Anthropic therefore launched Fable 5 with a new set of classifiers. Classifiers are separate AI systems. They detect potential misuse, including jailbreak attempts. They prevent the main model from responding to flagged requests. When Fable 5’s classifiers flag a request, the response is handled by Claude Opus 4.8 instead. The covered areas are cybersecurity, biology and chemistry, and distillation. Users are informed whenever a fallback occurs. For biology and chemistry, Fable 5 falls back to Opus 4.8 on most requests for now. Anthropic cites concern that the same dual-use queries could give uplift to malicious actors. It plans a trusted access program for biology, giving approved researchers Fable 5 without those safeguards. Anthropic tuned these safeguards conservatively. They will sometimes catch harmless requests. On average, they trigger in less than 5% of sessions. Anthropic says more than 95% of Fable sessions involve no fallback at all. For those sessions, Fable 5’s performance effectively matches Mythos 5. Anthropic red-teamed the classifiers extensively. An external bug bounty produced no universal jailbreaks in over 1,000 hours. A universal jailbreak lets a user interact with the model as if its safeguards were absent. Anthropic notes the UK AISI made progress toward one in a brief testing window. Mythos 5 is the same model with cyber safeguards lifted. Anthropic describes it as having the strongest cybersecurity capabilities of any current model. It is deployed through Project Glasswing in collaboration with the US government. Use Cases These capabilities map to several concrete workflows for technical teams: Large-scale code migration: Long-horizon coding suits big refactors and cross-repo migrations. The Stripe example shows this at a 50-million-line scale. Agentic coding pipelines: Fewer turns and token efficiency help multi-step agent runs. GitHub reported autonomy and reliability on complex, long-horizon coding tasks. Finance and analytics work: Strong document and chart reasoning suits senior-level financial analysis. Hebbia and IMC cited gains on reasoning and trading-analysis tasks. Vision-to-code tasks: Rebuilding source from screenshots suits front-end reconstruction and figure extraction. The vision-only harness reduces tooling overhead. Long-running research agents: Persistent memory across millions of tokens suits multi-day research loops. Mythos 5 ran novel genomics work over a week of largely autonomous work. Comparison Table: Fable 5 vs. Mythos 5 vs. Opus 4.8 Attribute Claude Fable 5 Claude Mythos 5 Claude Opus 4.8 Model tier Mythos-class Mythos-class Opus class Underlying model Same as Mythos 5 Same as Fable 5 Opus 4.8 Availability Generally available Limited (Project Glasswing) Generally available Safety classifiers Active (cyber, bio/chem, distillation) Cyber safeguards lifted Opus-level safeguards

Software development has changed. Engineers no longer type most code by hand. They describe intent, and AI agents do the work. Modern tools plan tasks, edit across files, run tests, and open pull requests. Many now ship to production with limited supervision. No single tool fits every need. This guide covers the AI coding agents and platforms shaping development in 2026. Developer Tools Guide Top AI Coding Agents & Platforms — 2026 A practitioner’s field guide to the tools reshaping how software gets built. Development has shifted from typing code by hand to describing intent and letting agents do the work. Today’s tools plan tasks, edit across files, run tests, open pull requests, and ship to production — with limited supervision. No single tool fits every need. This guide walks through the platforms shaping AI-assisted development in 2026 — what each does and where it fits. Use the arrows or dots below to explore → ★ Featured Pick Atoms Goes well beyond a single coding agent. Atoms deploys a coordinated team of AI agents — product management, system architecture, full-stack engineering, SEO, data analysis, and paid advertising. Describe a product in plain language and get a working, deployable app with user logins, data storage, and payments. Race Mode runs prompts across multiple models at once for the best output. 10% off with code MARKTECHPOST10 Try Atoms → Autonomous Engineer Devin AI by Cognition An autonomous AI software engineer, not an in-editor assistant. Give it a natural-language task or a linked ticket; it plans, then executes inside a sandboxed cloud environment with shell, browser, and editor. It runs subtasks in parallel, coordinates sub-agents, and opens pull requests. Best for well-defined bug fixes, features, and migrations. Visit Devin → In-Editor Assistant GitHub Copilot Real-time code suggestions and autocompletion, integrated directly into your editor. It predicts and generates snippets as you type, cutting boilerplate and keeping you in flow — and now extends into chat, pull request summaries, and agentic tasks. A strong default for incremental help inside an existing workflow. Visit GitHub Copilot → UI Building Magic Patterns Builds user-interface components faster from prompts and references. A library of reusable patterns cuts the time spent on repetitive front-end work, so teams move from idea to a working interface prototype with less manual effort and stay focused on the harder parts of a project. Visit Magic Patterns → Agentic IDE Windsurf by Cognition An agentic AI code editor built on a VS Code base. Its Cascade agent reads the whole repository, plans and applies multi-file edits, runs terminal commands, and verifies changes against tests — working across the project as a connected whole. Recent releases added parallel agent sessions and tighter integration with Cognition’s Devin. Visit Windsurf → Prototyping Uizard AI Focused on rapid prototyping for UI/UX designers. Turn text prompts, sketches, or screenshots into interactive prototypes, accelerating iteration and user testing. By lowering the barrier to clickable mockups, it helps teams validate concepts earlier and arrive at more user-centered designs before engineering begins. Visit Uizard → Cloud IDE Replit Agent Brings coding automation into Replit’s browser-based environment. It scaffolds projects, writes and edits code, installs dependencies, and runs apps with no local setup — removing the overhead of configuring a dev environment. Well suited to SME workflows and going from prompt to running app in one place. Visit Replit Agent → Evaluation & Observability Galileo AI An AI evaluation and observability platform rather than a code generator. Its Agentic Evaluations trace agents step by step, score tool-selection quality, detect errors in individual tool calls, and track session success, cost, and latency. Essential guardrails for teams shipping agents to production. Visit Galileo → Terminal-Native Warp An agentic development environment born out of the terminal. Use Warp’s built-in coding agent or bring your own CLI agent (Claude Code, Codex, Gemini CLI). It runs and manages multiple agents in parallel, indexes Git codebases for context, and spans setup through shipping. Available on macOS, Windows, and Linux. Visit Warp → Design-to-Code Lovable Dev Specializes in converting designs into functional applications, bridging design and engineering. By turning visual layouts into working front-end code, it streamlines UI/UX development and tightens the handoff — letting designers and developers collaborate closely and bring designs to life with minimal manual coding. Visit Lovable → Rapid Build Bolt New Known for a friendly interface and easy deployment, accessible to newcomers and experienced developers alike. It supports rapid prototyping in the browser and integrates with common environments, making it a low-friction path from idea to a shareable, running application during fast iteration cycles. Visit Bolt → Multi-Framework UI V0 Dev Supports multiple front-end frameworks, giving developers flexibility to pick the right tools per project. Generated components and interfaces slot into your existing stack, reducing the cost of moving from prompt to usable UI — a versatile choice for diverse applications without framework lock-in. Visit V0 → AI-First Editor Cursor An AI-first editor designed to keep you in control of the codebase. It offers multi-file editing and codebase awareness alongside version control, code reviews, and collaboration — keeping changes organized and maintainable. Built for developers who want substantial AI help while retaining oversight of structure and quality. Visit Cursor → Key Takeaways What to remember AI coding tools have moved past autocomplete — they plan, edit across files, test, and ship. No single tool fits every job — pick by task: autonomous engineer, agentic IDE, evaluation, or full product platform. Atoms stands out for end-to-end product building — a coordinated agent team, not just a code assistant. Atoms ships deployable apps from a prompt — logins, storage, payments, plus Race Mode across models. Recommendation: for the whole product lifecycle, start with Atoms — code MARKTECHPOST10 for 10% off. Try Atoms → ‹ 1 / 15 › MARKTECHPOST Practitioner-first AI/ML news, model releases & developer tools — trusted by 1M+ readers. Atoms* Atoms goes well beyond a single coding agent. It deploys a coordinated team of AI agents. These cover product management, system architecture,