Committee

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Why worms (and microbes) are catching on as a manure pollution solution Anthony Agueda, a third-generation California dairy farmer, pulls a rake through a bed of dark, wet wood chips to reveal a half-dozen squirming red earthworms. There are likely hundreds of thousands more wriggling just under the surface. The worms and microbes are part of a “vermifiltration” system that cleans manure wastewater. The approach may dramatically cut methane, nitrous oxide, and water pollution. Vermifiltration is just one of a variety of methods that farmers, companies, and scientists are employing to drive down manure pollution as the livestock industry faces growing pressure to address the environmental harms from one of the smelliest parts of the business. Explore how the humble earthworm could reshape the future of sustainable farming. —James Temple MIT Technology Review Narrated: geoengineering gets a reality check Solar geoengineering, the controversial idea that we could deliberately intervene in the climate system to counteract global warming, is moving beyond computer simulations and into the practical engineering challenges required to make it real. Researchers are now working on aircraft, materials, and other systems for solar geoengineering. But as they delve into these details, they’re finding that even early deployment would require significant new infrastructure, time, and investment. —James Temple This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 The Trump administration has lifted restrictions on OpenAI’s GPT 5.6The green light came after additional testing and meetings. (Axios)+ OpenAI subsequently said it will launch widely tomorrow. (Bloomberg $) + The rollout had been delayed due to security concerns. (Verge)+ Does AI know too much? (MIT Technology Review) 2 China is looking at curbing overseas access to its top AI modelsAlibaba, ByteDance, and Z.ai attended meetings about the plan. (Reuters $)+ Beijing is also weighing the security risks of open-weight AI. (SCMP)+ And has issued a “backdoor” security alert over Claude Code. (CNBC) 3 European NATO allies have unveiled a $50 billion high-tech missile planThey will engineer stealth and high-speed hypersonic weapons. (BBC)+ Which can strike targets at least 300 km away. (Reuters $)+ The Dutch and British are also developing amphibious ships. (Bloomberg $) 4 Meta is testing “super sensing” AI glasses that record every momentIt plans to disable privacy LEDs that alert people when they’re “on.” (FT $)+ It’s also released an AI image generator. (NYT $)+ Which lets anyone use your Instagram photos in AI images. (Wired $) 5 China’s DeepSeek is developing its own AI chip, sources sayIt could reduce the company’s reliance on Nvidia and Huawei. (Bloomberg $)+ DeepSeek V4 was a win for Chinese chipmakers. (MIT Technology Review) 6 Wikipedia is fighting to survive the internet’s next eraIt’s under attack from MAGA, AI raids, and repressive regimes. (NYT $)+ AI has given Wikipedia a language problem. (MIT Technology Review) 7 SpaceX plans to launch its first model coproduced with CursorThe new frontier model could arrive as soon as this week. (Information $)+ It’s built with AI startup Cursor, which SpaceX is buying for $60 billion. (FT $) 8 A new academic “humanizer” tool can erase signs of AI-written textBut researchers are very divided over its potential impact. (Nature $) 9 Scientists have detected a mystery chemical on Pluto and TitanIt appears to absorb light in a way we don’t currently understand. (Wired $) 10 A Waymo robotaxi reportedly called the cops on drinking teensOfficers then approached the vehicle with guns drawn. (404 Media) Quote of the day “Parents do you know where your teens are? Waymo does!”  —Local police post on Facebook that a Waymo in California called the cops on two teenagers for “drinking and shooting from the vehicle.” One More Thing MICHAEL BYERS Your boss is watching Dora Manriquez has spent nine years driving for Uber and Lyft, where every ride she accepts or rejects is tracked by the apps she relies on for work. Having found herself unable to score enough better-­paying rides, she has had to file for bankruptcy.  App-based employers aren’t the only ones keeping a very close eye on workers today. Jobs today—whether in an office, a warehouse, or your car—can mean constant electronic surveillance with little transparency, and potentially with livelihood-ending consequences if your productivity flags. All that data is shifting the relationships between workers and managers—and protections are lagging. Read the full story on the widening power imbalance it’s created. —Rebecca Ackermann We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + Literary worlds collide in this marvellous Dr Seuss/Stephen King mashup.+ A daring snorkeler saved a dolphin from a suckerfish—and then celebrated with its whole pod.+ This clever musical project seamlessly constructs an original song from vocal snippets of 50 artists singing US city names.+ A long-lost wallet from 1970 was recently unearthed, creating a cute time capsule from its owner’s high school years.

Anthony Agueda, a third-generation California dairy farmer, pulls a rake through a bed of dark, wet wood chips on his family’s land in Hickman, a tiny town in the state’s agricultural heartland. He reaches down with both hands and pulls up a clump of muck, turning it over to reveal a half-dozen squirming red earthworms. There are likely hundreds of thousands more wriggling just under the surface of the three-foot mound of wood and crushed river rock before us, which stretches across the equivalent of six football fields. These natural materials form a biofilter that may dramatically cut the methane, nitrous oxide, and water pollution generated by the massive amounts of manure that hundreds of Holstein cows produce each day. Agueda’s family business, the Alberto Dairy, was one of the first cattle operations in California to adopt this approach to manure treatment, developed and patented by the Chilean company BioFiltro. Eight more of these so-called vermifiltration systems are already operating on US dairies, according to the company, while another 16 are under construction or set to be next year, nearly all of them in California.  Vermifiltration is just one of a variety of methods that farmers, companies, and scientists are employing to drive down manure pollution as the livestock industry faces growing pressure to address the environmental harms from one of the smelliest parts of the business. California, easily the nation’s largest milk producer, has established a handful of programs to promote their adoption, including one initiative that has funneled more than a billion dollars to farms. Researchers stress that much more work needs to be done to determine the most effective approaches, the trade-offs between them, and their success over the long term, under actual farm conditions. Agueda says that he and his family recognized the need to adopt new practices as environmental rules tightened. They were drawn to vermifiltration because it’s simple and relatively cheap compared with other, higher-tech options. “California daily farmers are constantly facing more and more regulation,” says Agueda, standing alongside one of the farm’s free-stall barns. “This makes me excited, because it shows how we are part of the solution.” The growing manure problem Manure is responsible for a significant portion of the climate pollution from livestock operations. The World Resources Institute estimates that manure management on dairy and swine farms accounts for 1.6% of the US’s greenhouse-gas emissions. Globally, manure storage and processing makes up about 10% of the livestock industry’s contributions to climate change.  “Farms have become larger in the past two decades or so, so there’s much more manure—and that has to be stored somewhere,” says Swati Hegde, the organization’s global manager of agricultural methane. Typically, cattle and swine farms spray manure into lagoons or tanks, creating a foul-smelling, low-oxygen slurry in which microorganisms known as methanogens thrive. They gobble up hydrogen, carbon dioxide, and other compounds and produce methane as a by-product. Other microbes in the mix produce smaller amounts of nitrous oxide. A pair of Holstein cows poke their heads through the rails of a free-stall barn at the Alberto Dairy.JOE PROUDMAN/UC DAVIS Both are particularly potent greenhouse gases, with as much as 30 to nearly 275 times the warming power of carbon dioxide, respectively, over a century. The slurry is often spread onto fields to add nutrients to the soil. When it’s done excessively or improperly, this part of the practice can pollute soil or groundwater with drug residues, pathogens like salmonella and E. coli, and nitrates. Nitrates that leach into drinking water have been linked to a variety of human health risks. And those that flow into rivers, lakes, and coastal waters can spawn algae blooms that poison fish, block sunlight, suck up oxygen, or form large coastal dead zones devoid of marine life. Policy drivers A number of regions, nations, and states have passed regulations or offered subsidies designed to limit the pollution from livestock manure, but so far, most of the major initiatives have focused on water contamination rather than greenhouse-gas emissions. The European Union, for instance, restricts the amount of manure that farmers can apply to fields and requires member nations to monitor nitrate levels in ground and surface water. The US’s Clean Water Act requires large livestock operations to obtain permits and develop manure management plans that limit pollution.  But California has arguably done the most to use government policy specifically to drive down the methane emissions from livestock. The dairy industry accounts for about 45% of the state’s pollution from the potent greenhouse gas, and more than half of that comes from manure, according to the government’s estimates.  In 2016, the state enacted a law that requires dairies, landfills, and other businesses to cut methane emissions 40% below 2013 levels by 2030, as part of a broader effort to reduce pollution from powerful but short-lived greenhouse gases. The measure directed the California Air Resources Board, the state’s main climate regulatory agency, to set up various incentive programs to encourage these industries to shift to cleaner practices.  “In terms of bang for your buck, short-term benefits, methane can go a long way toward reaching climate goals,” says Tawny Mata, director of California’s Office of Agricultural Resilience and Sustainability.  Between these various programs—and falling livestock numbers in the state—the dairy sector is on track to reduce annual methane emissions by the equivalent of 5 million metric tons of carbon dioxide by 2030, the state estimates. That would still fall about 4 million tons short of the target under the 2016 law. The downsides of dairy digesters Excluding the decline in herd populations—which has been driven by growing international competition and rising costs—the vast majority of California’s estimated methane reductions come from the use of what are known as anaerobic digesters. This technology entails covering the slurry lagoons to prevent methane from leaking into the air and then piping the biogas into separate vessels, where it’s cleaned and converted into natural gas.  Under California’s Low Carbon Fuel Standard program, dairies that use digesters to produce

With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future. Returning to the foundational elements of AI architecture—the structural framework required for deploying and managing reliable, integrated AI systems at scale—allows technology leaders to make astute decisions today while supporting a future of AI agents that can retrieve information, make decisions, and execute complex workflows across systems. Four elements of AI architecture you can count on The following capabilities provide a stable compass on the path to production-ready deployment, regardless of how the underlying technology evolves. 1. Prepare data for AI at scale Models are only as reliable as the data they can access, and poor data quality leads to AI hallucinations, bias, and unreliable outputs. Most enterprises rely on legacy systems, inconsistent data structures, fragmented ownership, and incomplete datasets, making it difficult to scale AI effectively. Powerful as it is, AI itself cannot solve these underlying data problems. As Adnan Adil, CIO of Elastic, explains: “The data is a durable part of AI architecture because without it, these models won’t run, won’t provide the right context, or won’t give the right level of services that we’re looking to implement.” Industry surveys consistently cite data quality as one of the greatest barriers to AI success. “The data quality has to be good; otherwise, the user loses confidence in the system,” says Adil. An effective AI strategy begins with connecting data across the organization and ensuring it is organized, accurate, governed, and accessible in real time. These considerations are most effective when built into models and architecture from the start. Scalable data architecture allows AI systems to evolve alongside the business and connect reliably to the internal information needed to deliver meaningful value. Gartner predicts that companies will abandon 60% of all AI projects through 2026 if they are not supported by AI-ready data. Avoiding that outcome includes clear data standards and ownership, clean and labeled data, and pipelines that support real-time retrieval. 2. Use context engineering to deliver the right data to every AI query Context engineering ensures that the model draws on the most pertinent information for each query, selecting and organizing the data needed to produce accurate answers efficiently. Effective context engineering shapes the inputs that guide AI reasoning and action. While prompt engineering focuses on how a request is worded, context engineering designs the entire information environment around the model: retrieving the right data and presenting it in a structured, machine-readable way. Many organizations are discovering that reliable AI depends as much on context quality as on the strength of the model. Context engineering relies on a modernized, unified data foundation as well as retrieval and memory systems such as retrieval augmented generation (RAG) and vector databases. It also requires careful prioritization to determine what information matters most, what should be excluded, and when different types of information should be used. Feeding models too much context can dilute relevant details, increase costs, and slow response times. “Minimum context, correct and current data, and machine-readable information are critical to effective context engineering,” Adil says. 3. Build AI governance and LLM observability in from the start Strong governance and LLM observability help organizations maintain control over how AI systems use data, monitor system performance, and identify problems before they affect operations. In the absence of clear controls around retrieval, workflows, and model usage, AI systems often process far more information than necessary. This inefficiency also drives up operating costs by requiring additional computing resources, often reflected in higher token consumption and API charges. Governance also works in tandem with robust security. AI expands the attack surface, introducing risks such as prompt-based data leakage, model vulnerabilities, and adversarial inputs. Protecting sensitive information requires strong access controls, monitoring, and oversight. Adil notes that essential controls — including those related to security, granular cost management, project controls, data security, and architecture—are frequently insufficient. For governance systems to support transparent, compliant, trustworthy, and cost-effective AI, organizations cannot leave them as a layer to add later. Governance structures need to be embedded into architecture, workflows, and decision-making processes from the outset. When governance is established from the start, it enables robust observability. Observability helps organizations understand how AI applications are performing in practice. Mechanisms for LLM observability and benchmarking allow teams to assess accuracy and utility over time, monitor adoption patterns, and adjust systems as conditions change. Observability also helps organizations gain trust by increasing visibility of model performance, behavior, and failure points. Furthermore, observability is essential to get ROI of AI initiatives, as the benefits of it are often indirect and business value depends heavily on how systems are adopted and used. Real-time visibility into AI behavior allows organizations to measure performance against expectations, identify gaps between intent and reality, and continuously refine systems as requirements evolve. In a 2026 report from Elastic, 85% of IT decision makers expect to enable LLM observability for their internal generative AI apps. “Observability is actually huge. We can use observability data for cost control, decision-making, and engineering efficiency,” Adil says. 4. Keep humans in the loop The thoughtful design, integration, and governance that maximize AI value demand specialized in-house expertise. Nearly 70% of respondents in Deloitte’s 2025 Tech Executive Survey report plan to grow teams in direct response to generative AI, a clear contrast to widely reported AI-related cuts. Adil agrees: “We think the people aspect is largely what’s going to make AI impactful going forward.” As AI systems become more embedded in operations, organizations need people who can govern workflows, evaluate outputs, redesign processes, and adapt systems as conditions change. Evolution toward increasingly autonomous tools requires teams skilled in prompt engineering, orchestration, and change management.  Talent adept at critical thinking and prepared to adapt with technology’s rapid advances will be in high demand. Although turnover brings in

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Your family’s $300 stake in OpenAI Sam Altman’s proposal that Americans should share in the wealth created by AI is back in the spotlight, with reports that he is discussing giving the US government a 5% stake in OpenAI. At the company’s current valuation, that stake would be worth roughly $320 per American household. The idea is meant to address concerns that AI companies are benefiting from human-generated work without compensating creators, while also easing fears that AI will cause a collapse of the labor market by providing a safety net.  The details, however, remain unclear. Indeed, the offer may be more powerful as a political narrative than as a policy plan. Read the full story on what the dividend proposal reveals about the future of AI. —James O’Donnell This article is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.  1 A leaked Treasury report compares the AI market to the dotcom bubble Which contradicts the administration’s public optimism about AI. (NOTUS)+ Fears that the market is overinflated are growing. (Reuters $)+ And AI profits are hiding bigger risks in earnings reports. (FT $)+ What even is the AI bubble? (MIT Technology Review) 2 Samsung profits have jumped 1,800% on booming AI chip salesIt just reported its third consecutive record quarterly profit. (BBC)+ But its shares slumped over fears that the AI boom will stall. (Reuters $)+ That boom has turned Samsung into a $1 trillion company. (CNBC) 3 A US cyber agency is using Mythos to audit government codeSources say CISA is tapping Anthropic’s model to search for bugs. (Reuters $)+ Agencies are using it despite Anthropic’s feud with the White House. (Axios) 4 Illinois’ governor has signed the nation’s strongest frontier AI lawIt’s designed to protect citizens from AI risks. (Gizmodo)+ US lawmakers are clashing over AI rules. (MIT Technology Review) 5 A hidden tracker in Claude Code has been exposed and removedIt secretly monitored users in China. (WP $)+ Critics said it shows Anthropic’s willingness to surveil users. (Ars Technica)+ The company has also found a hidden “thinking” space in Claude. (Axios) 6 Russia is suspected of flying drones over Europe from a shadow fleetThe flights were reportedly launched from commercial ships. (Ars Technica)+ Europe has a drone-filled vision for future wars. (MIT Technology Review) 7 A controversial AI “actor” is set to star in its first feature filmTilly Norwood will debut in a comedy-drama called “Misaligned.” (Variety)+ A major actors union has lambasted the AI creation. (NBC News) 8 AI costs are driving US companies toward Chinese modelsBusinesses are hunting for cheaper model alternatives. (CNBC)+ Chinese AI labs are betting big on open source. (MIT Technology Review) 9 Researchers have shown quantum proofs can beat classical onesThey found a problem that classical proofs can’t solve. (Quanta) 10 Earth will never be swallowed by the sun, according to new modelsBut it probably won’t be much fun to live here by that point anyway! (Wired $) Quote of the day “The goal might be to make machines in our image. But what I fear is that—perhaps without even quite noticing—we remake ourselves in theirs.”  —Reporter Sarah O’Connor sounds a note of caution in her new book, We Are Not Machines, the Guardian reports. One More Thing KATE DEHLER Adventures in the genetic time machine Eske Willerslev, a specialist in recovering DNA from old bones and objects, has made numerous breakthroughs. These include recovering the first more or less complete genome of an ancient human and 2.4-million-year-old genetic material from Greenland, revealing that today’s Arctic desert was once a forest with poplar, birch, and mastodons. These findings are part of a wave of discoveries from what’s being called an “ancient-DNA revolution.”  Beyond revealing stories of human migration and vanished ecosystems, scientists believe ancient DNA can unearth clues about modern diseases. It could even lead to a better food supply for our warming world. “And can we get that?” Willerslev asks. “Yes, I believe we can.” Discover how ancient DNA could rescue the future.  —Antonio Regalado We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + Underworld’s electric set from EDC Las Vegas 2026 has been released as a full concert video.+ This photographic journey through global soccer culture captures the mad passion of fandom around the world.+ Feeling unhinged? Me too. This playlist of gloriously intense classical music sympathetically captures the mood.+ If you’re looking for visual inspiration, this collection of graphic design archives from across the web is a goldmine.

Tools execute code.

Sakana AI has added a new feature called Sakana Translate to its chat service, Sakana Chat. It handles bidirectional translation across Japanese, English, and Chinese. The translation engine is Namazu, the company’s model series adapted for Japanese. Sakana Translate ships as a free web app. A single account unlocks all three of its modes. What is Sakana Translate Sakana Translate is a browser-based translation product, not a new base model. It runs on Namazu, Sakana AI’s Japan-adapted model series. The concept Sakana AI states is ‘deep translation for Japan.’ The goal goes beyond swapping words and sentence structures. It aims to carry context, tone, and register between languages. That focus targets a specific gap. Sakana AI argues that general tools often miss what makes Japanese distinctive. Examples include business honorifics, culturally specific concepts, abbreviations, and internet slang. Grammar may stay correct while interpersonal tone gets lost. The product bundles three functions into one screen: Translate, Proofread, and Ask. The Three Modes, Explained Each mode targets a different everyday task. The table below summarizes them. Mode What it does Key detail Best for Translate Converts pasted text between the three languages Up to ~5,000 Japanese characters, streaming output, history saved automatically Emails, slide decks, articles, web pages Proofread Refines a draft into a more natural version Changes shown with diff highlighting; adjusts tone, politeness, and formality Business email and English writing checks Ask Answers follow-up questions about a result Clarifies nuance, suggests alternatives, explains grammar in the same context Learning why a translation reads the way it does A few terms are worth unpacking for engineers new to this space. Streaming output means the translation appears progressively, token by token. You do not wait for the full result before reading. This mirrors how chat models return text. Diff highlighting shows exactly what changed. Additions and removals are marked inline, like a version-control diff. Proofread goes past grammar. It also tunes naturalness, politeness, and the register a reader expects. Ask removes the tool-switching problem. You no longer jump between a translator and a dictionary. Nuance questions get answered against the same source and output. You can try all three modes in the interactive demo below How Namazu Powers It Namazu is the engine, so its design matters here. Sakana AI is a Tokyo lab founded in 2023 by David Ha and Llion Jones. Namazu is not trained from scratch. It applies post-training to existing open-weight foundation models. Reported base models include DeepSeek-V3.1-Terminus, Llama 3.1 405B, and gpt-oss-120B. Post-training means adapting an already-trained model with further tuning. It is cheaper and faster than pre-training a model from zero. Sakana AI uses it to fit models to Japanese language and culture. Sakana AI first announced the Namazu series on March 24, 2026. Sakana Translate applies that same adaptation work to the translation problem. Benchmark and Performance Sakana AI team evaluated translation quality with a standard setup. It used XCOMET-XL on the WMT 2024 General Translation task data. Here is what those two names means: WMT 2024 General Translation is a shared task from the Conference on Machine Translation. Systems translate test sets drawn from several domains across many language pairs. It is a common yardstick for machine translation research. XCOMET-XL is a neural evaluation metric from Unbabel, with roughly 3.5B parameters. It is a learned model that scores translation quality. It outputs a score and also flags specific error spans. Scores run from 0 to 1, where higher is better. By the reported results, Sakana Translate landed in a score band close behind the leading models. Sakana AI describes this as competitive quality for a translation engine. Sakana AI also ran a qualitative check on everyday Japanese texts. It reports strengths in honorifics, cultural concepts, place names, proper nouns, and everyday context. https://sakana.ai/translate-release/ Use Cases With Examples Sakana AI published two concrete outputs. Both show the tone-preservation goal in action. 1. Business email (Japanese → English). The source is a polite, indirect request. It uses set business phrases such as o-mitsumori haiken shimashita and katte na onegai desu ga. Source: お見積り拝見しました。勝手なお願いですが、もう少しご相談できますか？ご予算あれば社内で調整しますので、お聞かせください。 Sakana Translate: I saw your quote. This is a bit of a selfish request, but could we talk a bit more? If you have a budget in mind, I can work on it internally, so please let me know. Sakana AI notes the polite tone is preserved. A flatter translation would drop that register. 2. Internet slang (English → Japanese). The source uses casual shorthand from a group chat. Source: Iykyk, honestly. It’s an inside joke from the group chat. Sakana Translate: まあ、わかる人にはわかるよね。グループチャットの内輪ネタだから。 The output keeps the same casual temperature in conversational Japanese. Beyond these, practical scenarios follow naturally. A support agent can translate a long client thread in one paste. A developer can proofread an English release note before publishing. A learner can ask why a phrase carries a certain tone. Comparison: What Sakana Translate Targets The table below frames Sakana AI’s stated design focus. The left column describes common behavior in general-purpose tools. The right column reflects Sakana AI’s own claims, not a head-to-head benchmark. Dimension Typical general-purpose MT Sakana Translate’s stated focus Honorifics Grammar correct, tone often flattened Preserves polite and humble registers Cultural concepts Literal or generic renderings Adapted to Japanese context Slang and abbreviations Frequently mistranslated Matched to the original tone Workflow Separate translator and dictionary Translate, proofread, and ask in one screen Access Varies Free web app, single account, three languages A Look at the Evaluation Metric Sakana Translate has no public API yet. Sakana AI lists API access as a future, enterprise-focused plan. You can still reproduce the evaluation method it used. XCOMET-XL is open and runnable. The snippet below scores one translation with it. Copy CodeCopiedUse a different Browser # pip install “unbabel-comet>=2.2.0” # XCOMET-XL is gated on Hugging Face: accept its license, # then run `huggingface-cli login` before download_model(). from comet import download_model, load_from_checkpoint model_path = download_model(“Unbabel/XCOMET-XL”) # ~3.5B params model = load_from_checkpoint(model_path) data = [{ “src”: “お見積り拝見しました。”, “mt”: “I saw your quote.”,

You build an agent with five tools.