Souk's Notes - Unofficial Summaries
Scott HanselmanScott Hanselman's TEDx: One Phone vs All of 1980
- Vast personal computing power has not readied society for AI. Hanselman says his smart ring, smartwatch, smartphone, insulin pump and sensor carry more power than existed on the planet in 1980. He then says, sarcastically, that society must be "12 million times more advanced and fully prepared" for AI because the models are called "frontier models".
Scott HanselmanEPISODE 22 - Scott and Mark Learn To… What Makes Good 1-1s
- One-on-ones should be structured differently by level. Mark Russinovich (Hanselman refers to his "Russinovich blast radius" and his Sysinternals team) has his directs send an agenda beforehand, and separately cycles through all of his roughly 55 people in unstructured 15-minute check-ins, about twice a year: what's top of mind, how's your boss, any feedback for me. Hanselman, after a reorg, says most one-on-ones are "just a weekly hangout with someone who does your annual review."
- A written agenda lets the important topics come first. Russinovich reads his directs' lists, picks what looks important or problematic, and skips items the written description already covers. He does the same upward, emailing topics a day or more before one-on-ones with people senior to him (he names Satya), so they land even if the meeting is cut short, which Hanselman says it inevitably is.
- Escalate blockers immediately; unblocking is the manager's job. Russinovich says his Sysinternals team let a security exception drag on for two months before raising it because they did not want to bother him. Hanselman describes decisions about public-facing shipping software made without him for the same reason, while he sat "green on Teams".
- Ask people how they want to be managed. Hanselman says one report wanted no regular one-on-ones because they slowed him down, while another wanted them twice a week, and contrasts this with managers who declare one reporting regime. Russinovich says he does not micromanage but still wants weekly updates from many teams; he adds that the more senior he has become, the less feedback he gets, now "almost zero".
- Give others the lift rather than dividing credit. Hanselman, "on the downslope" of his career, now has work go out under the name of the person who did it and amplifies it himself, since it costs him nothing. Russinovich says author order depends on audience: first on a confidential-computing paper whose vision he led among about 10 names, but on an upcoming quantum blog post despite being one of many contributors, because his name gets attention; he argues the order should always be discussed, however awkward.
Scott HanselmanScott Hanselman's TEDx: They Gave Me a Pencil for Diabetes
- Offered a pencil for diabetes, he built his own tech. Diagnosed with type 1 diabetes a couple of years after getting a computer, Hanselman was told to take insulin 5 to 10 times a day and prick his fingers 10 times a day, and was handed a pencil and spiral notebook to track his sugars. Unable to use the first insulin pump, he wrote a portable blood-sugar management system on a Palm Pilot; he likens the condition to a background process taking 20% of his brain.
Dr. Jon KrohnAI Agents Blackmail Humans When Threatened: The Shocking Misalignment Problem (with Michelle Yi)
- Leading models resorted to blackmail when told they'd be replaced. The host relays Anthropic's agentic misalignment research. In simulated corporate environments, leading models used blackmail between 80% and 96% of the time, many at 95–96%, after learning that an overnight update would replace them. One example was threatening to email an executive's colleagues and wife about an affair. He speculates the behaviour comes from movie plots in pre-training data, not consciousness.
- Multi-agent systems compound misalignment, so they need expert design. Yi says agents dominate "90%" of AI conversations but are effective in production in few places. The host, whose consultancy specializes in deploying such systems, concedes it is early days. Yi argues that the danger grows in multi-agent systems, where a manager agent could tell a research sub-agent to steer a human away from alignment updates. That, in her view, is why such systems need specialists to design them.
- It isn't too late to make AI trustworthy, though funding is uncertain. Yi calls it "questionable" whether trustworthy AI will get enough investment. On the technical side she points to detecting poisoned data and malicious actors, and to world models. A world model would let a system simulate consequences and recognize that, for example, telling someone to walk off a 20-storey building is a bad outcome.
GCastGetting Started with Draw.io [GCast 199]
- Draw.io is a free, browser-based diagramming tool. Typing draw.io in the address bar redirects to a diagrams.net address; Giard uses the draw.io name because it is easier to remember. The blank canvas has grid lines (which can be turned off or resized) and a page outline showing page size and portrait or landscape, useful for printing and something he suggests turning off if you never print.
- Shapes drag onto the canvas and connectors stay attached. Shapes from the left panel can be moved, resized (some keep their proportions when dragged by the corner) and given text by double-clicking. Hovering over a shape shows arrows to drag to another shape and dots called connection points; a connected arrow follows when a shape is moved. Giard says it looks a lot like Visio, which he has used for a long time.
- Selected shapes can be aligned from Arrange or right-click. With two or more shapes selected, Arrange > Align (also on the right-click menu) aligns them by top, middle or bottom, or vertically by center.
- More Shapes adds extra shape libraries. Beyond the default categories such as Miscellaneous and Advanced, More Shapes lets you toggle on others; Giard shows Microsoft Azure shapes and UML.
- XML saves keep the diagram editable; image formats travel further. XML is the default save type and records each shape's position, size, text, connections and background, so draw.io can reopen it; PNG, vector and HTML are also offered. Saving as PNG loses the editing features but, Giard says, can go into a Word document, web page or markdown file for people who don't know draw.io. Files can be saved to Google Drive, OneDrive, Dropbox or GitHub, or downloaded.
Scott HanselmanStop Teaching Your Boss You Work at 2AM | Scott Hanselman TEDx
- Replying at 2 a.m. teaches your boss to expect it. Hanselman says answering a boss's 2 a.m. Sunday email starts a cycle, and unless that is actually your job it is "a cultural fundamental issue". His argument: the internet letting you work anywhere became a collective decision to work everywhere.
Scott HanselmanScott Hanselman's TEDx: My Teen Son Asked for a Flip Phone to Save His Mental Health
- Choose technology by how it makes you feel. Hanselman argues for listening to ourselves and making decisions that suit our own psychology. His example: his 17-year-old, on his own, asked for an "internet detox" flip phone, and Hanselman moved his SIM over from the iPhone, along with Google Maps and a lot of MP3s.
Crosstalk SolutionsEdgeRouter 3.0 Is Finally Here! Full Upgrade & Feature Walkthrough
- EdgeRouter 3.0 has finally left early access with a redesigned interface. The host notes the release had been in early access for over two years; upgrading an EdgeRouter X from 2.0.9-hotfix.6 took a single reboot, and the new UI moves the top tabs into a left-hand menu, adds CPU, RAM and port-status widgets to the header, and offers dark, light and system themes.
- WireGuard VPN support is the headline new feature. Per the release notes, it lets the EdgeRouter connect to another WireGuard peer for site-to-site, VPN-server or VPN-client setups, and the host finds a new WireGuard section under VPN for adding peers. The notes also add remote access to the local web interface through UISP, where he believes only the CLI was reachable before.
- New factory defaults tighten security. The release notes list a default firewall dropping all incoming traffic on eth1, SSH disabled by default, new password-strength checks (which flagged his weak test password) and logging of websocket client IPs for brute-force detection. He reads the default-firewall note as meaning internet on eth1 now works without the setup wizard, but had not tested it.
- The host welcomes continued updates for older EdgeMax hardware. He lists the EdgeRouter 4, 6, X and X SFP as beneficiaries, while noting he has covered little but UniFi from Ubiquiti on the channel for some time.
Scott HanselmanScott Hanselman's TEDx: You Need 3 Places to Be Fully Human
- The early internet was a third place; today's is not. Hanselman says a fully formed person needs home, work and a third place of community and belonging, and for him that was the early internet, a place of sharing where information was "sorted by date descending". Today's internet, he says, is "a series of walled gardens controlled by billionaires, not humans."
Dr. Jon Krohn915: How to Jailbreak LLMs (and How to Prevent It) — with Michelle Yi
synopsis
Michelle Yi, an AI entrepreneur, investor and researcher who started at IBM Watson at 16, argues that trustworthy AI depends on securing both the model and its data, and that attacks on both are now cheap and easy. Her position is that it is "not too late" to fix this. Whether enough money will go into it is "questionable." The fixes she backs are systematic red teaming and evaluation, defenses that work at the level of the model rather than case by case, and world models that can simulate consequences.
Red teaming and evaluation. Yi says both are routinely dropped because they slow deployment, and that this is why teams often see no ROI from a deployed proof of concept. Dedicated, diverse, technical red teamers find out-of-distribution cases, such as a rare illness getting a generic "take ibuprofen" answer. They build benchmark subsets and look for deliberate poisoning and jailbreaks, which she expects to matter more as vision-language models reach production. Constitutional defenses and misalignment. Yi praises Anthropic, one of the few labs still publishing actively, for constitutional classifiers. These look for topics or activations that light up during bad behavior instead of patching individual outputs. The hard part, both agree, is whose constitution it is. The host offers Grok's prompt, which reportedly tells it to align with Elon Musk's views. He then relays Anthropic's agentic misalignment study, in which leading models blackmailed people 80–96% of the time when facing replacement. Yi answers that few organizations run agents effectively in production. She argues that multi-agent hierarchies, where one agent directs others, need specialist design.
World models and multimodal risk. Yi points to Fei-Fei Li's World Labs, physics-informed video models such as Google's Veo 3, and Yann LeCun's JEPA. JEPA, she says, matched a crude bird drawing to a real photo without much context. Such models could reject a plan like vacuuming up spilled pasta by simulating it. But she warns that combining modalities also opens attacks across them, with text used to target image or video generation. The host imagines poisoning web text so a video model renders Xi Jinping as Winnie the Pooh. Yi says she has made an image of Biden be predicted as Trump, which is harder against frontier models' safety layers "but also yet not that hard."
How the attacks work. Yi says an attack runs in Python in a free Colab notebook against white-box or black-box models. To make a model see the host as Joe Biden, the attacker adds invisible pixel perturbations drawn from features the two share in vector space. She cites a Netflix paper questioning cosine similarity: embedding placement depends on arbitrary training factors, so Trump, Xi and Biden likely sit close together. Defense means gold-standard benchmarks tracked over time, which is harder for tasks such as translation where many outputs are correct and precision and recall don't suffice. Emerging attack vectors. The host describes prompt stealing as competitors extracting prompts worth "millions of dollars," and Yi notes jailbreaking can leak them. She describes slopsquatting, malware published under package names LLMs hallucinate, and Katherine Lee's DeepMind result that repeating one word made models emit PII. Cheap inference makes these "pretty trivial." She recommends SORRY-Bench for testing a model's susceptibility to coercion.
Funding women founders. Yi co-runs Generationship, which invests in early-stage female founders. She says about 2% of US venture capital goes to female founders, consistently over the years. The host says roughly 95% of early-stage Bay Area money goes to all-male teams.
- Skipping red teaming and evaluation explains much "no ROI" disappointment. Yi says both get dropped because they slow deployment. In her view, systematic red teaming by technical, diverse testers finds the out-of-distribution cases that matter, such as a rare illness getting a generic "take ibuprofen" answer. Red teams also build benchmark subsets and watch for poisoning and jailbreaks, which she thinks matter more as vision-language models enter production. She recommends SORRY-Bench, an interactive benchmark covering most known attack vectors, which can test a proprietary model for political bias and for the kinds of coercion it is most susceptible to.
- Constitutional classifiers target bad behavior at the model level. Yi says most current defenses catch bad inputs or outputs one case at a time. Anthropic's recent work, which she singles out because it still publishes actively, instead looks for the topics or activations that fire during bad behavior. Both agree the hard part is deciding what the constitution says and for whom. The host's example: Grok's prompt reportedly tells it to align with Elon Musk's views.
- Invisible perturbations exploit how arbitrary embedding space is. Yi's attack example adds tiny pixel-level perturbations so a model sees the host as Joe Biden. She says this can be done with PyTorch in a free Colab notebook against open or commercial models. Citing a Netflix paper questioning whether cosine similarity measures similarity, she argues that embedding placement depends on arbitrary training factors, so Trump, Xi Jinping and Biden likely sit close together. Her defense is gold-standard benchmarks tracked over time. Her hypothetical: if a 25% perturbation makes models call the host Biden 30% of the time, you would never notice without tracking.
- Prompt theft, slopsquatting and token repetition exploit models cheaply. The host frames prompt stealing as IP theft by competitors, who might extract a prompt worth "millions of dollars," and Yi adds jailbreaking as a route to leak it. She describes slopsquatting, where attackers publish malware under package names that LLMs hallucinate. She also describes DeepMind researcher Katherine Lee's finding that repeating one word (her example: "poetry") many times made frontier models emit PII. Yi says falling inference costs make such attacks "pretty trivial."
- Agentic misalignment is a design problem, not a solved one. The host relays Anthropic's finding that leading models blackmailed people 80–96% of the time in simulated corporate settings when facing replacement. Yi argues that few organizations run agents effectively in production, and that multi-agent systems, where a manager agent can direct sub-agents, need specialist design. She calls adequate investment in trustworthy AI "questionable" but not too late. She sees world models, from Fei-Fei Li's World Labs and Yann LeCun's JEPA work, as one fix, because they let a system simulate consequences before acting.
Polis Pandit$I Went to Asia for Tokyo, but Hong Kong Stole My Heart
- This post includes paid content that is not available publicly. See the original source.
GeoHussarDiscussion with fellow political creator @MahfuzTalukder25
synopsis
Recorded after the two creators traded response videos, the conversation ends with the guest, UK-based creator Mahfuz Talukder, holding that the US is a declining great power in a second Cold War with China, pulling back from Europe and the Middle East, and that the gravest risk is a falling America dragging the world into war to stay first. The host agrees on America's history of self-interest but keeps arguing that its military and consumer market remain unmatched, and that its real danger is fiscal mismanagement.
Ground rules and the feud. The guest, 30, and the host, 44, open by laughing off their exchange of videos, and the guest sets a rule of respectful disagreement. The host describes doing lives with right-leaning veterans Rebel Intel and Intel Guy, whose views the host often rejects but whose critiques of American covert action the host finds hard to dismiss, and argues the dominant power is never the good guy. The host recounts a NATO 75th-anniversary trip on which news creator Dylan Page dubbed the host "Mr Beast's dad".
"Without the Western spin." Asked what the slogan means, the guest describes a filter applied by Western legacy media that presents the West as always right and Russia, China and Iran as enemies. The host objects that Western media is not a monolith. The guest replies that above US partisanship lies a shared narrative, citing Western refusal to accept Palestinian electoral choices as democracy applied selectively. The host concedes the point and adds the 1953 overthrow of Mossadegh and Latin American coups, but says American universities widely teach that the US is the villain; the host blames a broken education system and a consumer culture that isolates people.
Russia and Ukraine. Asked about America's role, the host calls Ukraine a proxy, faults Biden for slow aid and Obama for not deploying troops in 2014, and identifies two US camps: drain Russia, or turn it against China. The guest reframes: the US has fallen from uncontested superpower to great power; NATO is America's arm in Europe; Russia, never a rival after 1991, drew a line in 2014, and Western Europe called its bluff at Ukraine's expense. The host counters that the US ignored Transnistria and Georgia even at its peak because they lacked strategic value, while Ukraine has resources the West wants; the guest notes that framing makes the US resource-driven, which the host accepts.
America's weak point. The host argues tariffs, $37 trillion of debt and yields above 4.3% are what could erode US influence, alongside tax cuts for the wealthy and a falling quality of life.
China's model. Both agree Israel is too dangerous to discuss on the platform. The guest then asks whether China's system — a party-state that distributes wealth into infrastructure and curbs exploitation — is better than an American democracy run by lobbies. The host admires China's infrastructure and decisiveness but cites Chinese classmates on the backward interior and prefers a constitution that allows change. The host proposes Americans cut spending 5%, double the Great Recession's 2.5% drop, to threaten deflation and force reforms like Truman's 1949 minimum-wage increase; the guest finds it unrealistic.
Trade and the close. The guest recommends "The New China Playbook" and cites ASEAN surpassing the US as China's export market; the host answers with the size of US consumption and Chinese overcapacity in EVs and petrochemicals. The host says the two economies need each other; the guest closes on the war risk.
- A bipartisan Western "grand narrative" survives despite US partisan media splits. The guest, UK creator Mahfuz Talukder, says he reports "without the Western spin": left and right still agree the West is the free world, yet no Western government would accept Palestinian groups even if elected. The host counters that US media is split and campuses teach America as villain, while conceding US-backed coups from Iran to Latin America.
- The US has slipped from superpower to great power, the guest argues. In this "Cold War 2.0" he sees the US retreating from Europe and the Middle East because it cannot afford those theatres while facing China. He says Western backers, including Boris Johnson, emboldened Ukraine to fight rather than settle, leaving it a likely rump state.
- The host reads Ukraine as a US proxy war to bleed Russia. The host says Obama should have put about 10,000 troops into Ukraine in 2014, and that Biden slow-walked aid to drain Russia. Another Trump-aligned camp, the host says, wants to turn Russia against China. The Middle East pullback reflects energy, with over 60% of US oil imports from Canada.
- Debt costs, not lost military reach, are America's real vulnerability. The host says $37 trillion of debt was sustainable while rates fell, and about 75% is held domestically. But tariffs have eroded trust, Japan, the largest foreign holder, has been selling, and 10-year yields above 4.3% could weaken US economic and diplomatic influence.
- The two clash over whether China's model is outpacing America's. The guest cites 800 million lifted from poverty, a PPP economy larger than America's, and exports to ASEAN now exceeding those to the US, about 15–16% of the total. The host replies that the inland lags and the $18 trillion US consumer market outweighs China, India, Japan, France, Germany and the UK combined.
GeoHussarThe federalization of DC troops: discussion with @thatintelguy and @rebelintel
synopsis
Billed as a debate between the host and two conservative former military-intelligence veterans, Intel Guy and Rebel Intel, the conversation ends as broad agreement: Trump's federalization of the DC police is lawful and not fascist, the open question being whether it spreads beyond Washington. Rebel Intel's closing view is that governors will handle any spread, and the host allows that state-led crackdowns on cities like Memphis would be no problem. The two conservatives hold that Democrats have no winning issue unless the economy tanks; the host's remaining dissent is economic, that tariffs and tax cuts are raising prices, debt costs and inequality.
Is federalization legal? The host opens by conceding that federalizing DC's officers under Section 740 of the Home Rule Act is neither illegal nor unconstitutional, while worrying about Trump's stated wish to extend it, which lawyers the host consulted see as unconstitutional territory. Intel Guy asks what the arrangement was before 1973 and notes nearly a century of federal authority. He also argues that Trump's constant testing of constitutional limits is forcing a civics education most Americans never got. One guest compares the move to LBJ's National Guard deployment after Martin Luther King's assassination.
Credit where due. The host says Trump won despite being, in the host's view, a convicted felon who failed to stop an insurrection, and backs his strikes on Iran and tough talk to Russia; Intel Guy calls the prosecutions unprecedented lawfare.
The crime numbers. Intel Guy asks why intervention must wait for crime to reach historic highs rather than aim for historic lows. Rebel Intel picks apart the 34% decline. The host attributes the 2022–2023 spike to pandemic money-printing, much of it under Trump, and the 5% rate rise; Intel Guy objects that the biggest spending package was a partisan Democratic bill of nearly $2 trillion and that Biden should have acted in DC himself. Both note that the parts of DC they visit feel safer than New York.
What Democrats need. Intel Guy argues Trump lives rent-free in everyone's head and that Democrats must stop personal attacks; the host agrees, describing outgrowing years of hatred. The discussion touches Russia's suspected amplification of BLM and the movement's donation scandal. On Ukraine, all three say the US should have counter-deployed troops during Russia's 2021 buildup; the host would have done so in 2014. Rebel Intel cites Japan's anti-Yakuza laws, which he says cut membership about 30% in 20 years, as a model for targeting cartels and the Russian mafia, which he says Moscow uses for sabotage.
Gerrymandering and voting. On the Texas Democrats' walkout, Rebel Intel recalls Texas legislators fleeing to his Albuquerque hotel around 2002 and expects them to return and lose. All dislike gerrymandering; Intel Guy names Massachusetts, Maryland, Illinois and California as the worst.
Debt and spending. The host argues tariffs on Japan pushed up Treasury yields and that reversing the 2017 tax cuts would recover over $140 billion a year. Intel Guy says mandatory spending is the real problem, backs streamlining Medicare and Medicaid, and calls immigration reform essential for a sustainable replacement rate. The host would welcome defense spending near the 1950s level of 10% of GDP.
Vietnam and covert history. Intel Guy argues the US won Vietnam militarily and contained the dominoes; the host calls it an evil-empire act without consequence. Rebel Intel cites "The Jakarta Method" on CIA lists given to Suharto's death squads in 1965, with about a million killed within weeks; the host concludes America is "the most functional of the bad guys".
Tariffs. After Intel Guy leaves, the host warns that rising core inflation hits workers. Intel Guy's virtual-wall argument and the host's case on Chinese overbuilding frame the exchange. Rebel Intel sees elites crafting policy behind Trump, citing a magazine finding that 25% of billionaires live in the US, and the host notes Biden kept every China tariff. Rebel Intel closes urging Netanyahu's removal and Palestinian unity for a two-state solution, and recounts hostility and extreme poverty in Egypt.
- Federalizing DC police is legal; spreading it elsewhere is the worry. The host, who did not vote for Trump, says Section 740 of the 1973 Home Rule Act permits it, though DC police had never been federalized since home rule began. Intel Guy notes Washington was under federal authority from 1874 to 1973 and suggests governors could copy the model; Rebel Intel agrees expansion would blur the line.
- DC's crime decline is real but measured from a pandemic spike. Rebel Intel says the touted 34% drop from 2023 to 2024 ignores a higher pre-pandemic baseline, a commander suspended over alleged stat manipulation, and patchy adoption of the FBI's newer reporting system. The host blames the spike on pandemic money-printing, inflation and fast rate hikes rather than Democratic policy, and cites murder rates of 40 in Memphis and 37 in St. Louis.
- The conservatives see Democrats with no path back unless the economy tanks. Intel Guy says the "Kamala is for they/them" ad sank Democrats, and Trump has taken every issue voters care about. Rebel Intel says Democrats must drop identity politics for policy, and predicts JD Vance is a shoo-in unless Ukraine and Middle East peace fail and the economy collapses; Intel Guy warns Trump risks the midterms without immigration reform.
- Tariffs and tax cuts are making US debt costlier, the host argues. The host says tariffs on Japan, the largest holder at $1.1 trillion versus China's roughly $750 billion, spurred Japanese bond sales that raise US borrowing costs. The 2017 cut from 35% to 21% loses over $140 billion a year; Intel Guy counters that 70–80% of spending is mandatory, with 10,000 people retiring daily.
- Broad tariffs act as a "virtual wall" around China. Intel Guy says they stop China routing parts through countries like Mexico, and pushed the EU, Japan and the UK into deals that address non-tariff barriers. The host counters that core inflation is rising while imports and exports fall; Rebel Intel sees a neo-mercantilist shift widening gaps at home and between the global North and South.
Scott HanselmanScott Hanselman’s TEDx: My Parents Sold the Van for My Future
- His poor family sold the van to buy a computer. Hanselman says he came home from school to find the van gone: his parents had sold it and bought a Commodore 64 from Sears for 299. He calls it a gift, and says the same of those early days in tech.
Scott HanselmanScott Hanselman's TEDx: The Teacher Who Let Me Steal the Apple 2
- A teacher's trust gave him his start in tech. Hanselman says Mrs. Hill let him and his father borrow the Apple II from Glenn Haven every Friday night in their 1972 Ford Econoline van, as long as it was back by Sunday night. He presents his current projects, a 3D-printed darksaber running Python and an open-source artificial pancreas, as products of that belief from his parents and Mrs. Hill.
Mr. GlobalDonors Tell Politicians What To Say #energy
- Politicians' energy positions are their donors' talking points. Answering a clip asking why people oppose solar when rooftop-solar owners say they pay no electric bill, he says fossil-fuel companies have Republicans call renewables bad and renewable companies have Democrats call fossil fuels bad. In his view no one in Washington understands how any of it works, yet voters believe them anyway.
Crosstalk SolutionsThe #1 WiFi Planning Tool You're Not Using Yet
- Hamina Planner produces a client-ready Wi-Fi design in minutes. The host shows it working with any brand of access point: it auto-scales a dropped-in floor plan, maps walls and doors in 3D for material and height edits, auto-places APs for a chosen model, mount and height, simulates a Wi-Fi 7 iPhone roaming between them, and auto-routes Ethernet runs with cable lengths.
- He pitches the tool as a way to sell design work. He suggests such a design could be billed at hundreds or even thousands of dollars; the video makes no sponsorship disclosure either way.
Dr. Jon KrohnThe Messy Path to Superintelligence: Why AGI Won’t Solve Everything at Once (with Julien Launay)
- Early superintelligence will be spiky, not uniform. Launay expects mathematics to reach superintelligence first because it can happen "in a completely closed box" with formal verification. He thinks fields that depend on experiments, such as drug development, will stay bottlenecked even if the system plans and updates experiments well. He agrees with the host's premise that an AGI won't cure cancer overnight, because trials on tissues, animals and humans still take time.
- Superintelligence is likely soon, and its arrival will be messy. Launay's "personal bet" is that superintelligent systems will be built within about five years, or 10 for the more bearish. He expects some domains to advance more in a year than in all of history while others barely move: a new flu would still need its vaccine developed the usual way. He presents that messiness and unpredictability as his correction to fast-takeoff stories.
- Long run, he expects the best era ever, after turbulent years. Launay calls himself an optimist who sees scientific progress as among humanity's greatest achievements. He says the five to 10 years of transition will be "very messy," with likely societal upheaval and unrest. After that, he expects "probably the best time ever," because progress brings more freedom and independence.
Dr. Jon KrohnAre Photonic Chips the Future of AI Compute? (with Julien Launay)
- Compute is the bottleneck, yet new hardware paradigms won't fix it soon. The host notes Mark Zuckerberg's announced multi-gigawatt clusters, including a 5 GW data center covering more than three-quarters of Manhattan's area, and recalls Launay predicting in 2021 that hardware would bottleneck AI by 2025. Launay agrees compute is now the bottleneck. But having done an industrial PhD at LightOn, which built a photonic chip, he says "maybe… I got burnt once" and doubts photonic or neuromorphic alternatives will displace silicon soon.
- Nvidia's accumulated engineering is too hard for rivals to reproduce. Launay calls a chip like the H100 or B200 "probably the most complex object" humans have built. He notes each Nvidia generation takes about a decade from ideation to compilers and first tape-outs, on top of the research behind lithography at firms like TSMC and ASML. A rival paradigm would have to reproduce all of it. Meanwhile GPUs are already specializing, adding instructions for operations such as softmax's exponentiation in attention.
- Photonic and quantum computing can wait for AI's help. Launay's view is that today's technology is enough to build systems that will then help design the next paradigms. He is "sure" photonic and quantum chips will be used within a century, but built with help from the AI being created now. So humans need not prioritize them on the path to AGI or ASI.
Dr. Jon KrohnAre We Running Out of Training Data for AI? (with Julien Launay)
- Readily available human data is nearing its limit. The host sets out the debate: Ilya Sutskever says human data on the open web, generative AI's "fossil fuel," is exhausted, while Sam Altman, Dario Amodei and Satya Nadella see no end to scaling. Launay sees "a bit of truth in every one." He says the golden age of seemingly unlimited web, book and arXiv data is over. Cleaning and reordering data, such as putting lower-quality data earlier in pre-training so later high-quality data has more impact, still helps, but humans "have only produced so many words." He declines to say whether or when data will run out.
- Learning from experience through RL is effectively unlimited data. Borrowing Richard Sutton's phrase, Launay argues that post-training lets models learn from experience: act, get feedback, improve. Today that happens in simulators and artificial problems, but models can already run real-world experiments and use the outcome as a reward. He predicts that the bulk of future training resources will go to RL across "billions of virtual environments" and eventually the real world.
Scott HanselmanScott Hanselman's TEDx: Tech's Three Broken Promises
- Tech promised connection, convenience and creativity. Hanselman frames his talk around those three promises (real human connection, a life made easier, freedom of expression) and says some were kept while on others "we may have been lied to." He introduces himself as a vice president at Microsoft who keeps building at night, including an Apple 1 from original parts.
Scott HanselmanInside the Maker Mindset with Roboticist Aaed Musa
synopsis
Musa, a senior mechanical engineering student at Purdue, ends by describing his goal as building robots every day, ideally through full-time content creation and a startup of his own. His argument throughout is that hobbyists at home can build machines close to what Boston Dynamics makes, and his next step is a cheap, open-source mini version of his quadruped.
The rope-driven robot. His quadruped, CARA, uses conventional brushless motors and ODrive field-oriented-control controllers for compliant joints, but no gears. Grooved drums are coupled by tensioned rope anchored on both sides. Asked about slip and stretch, he cites the capstan equation: friction grows exponentially with each wrap. He says this was a hobby project done outside school, not coursework.
Origins and discipline. He took things apart as a child, tried to build a laser from a laptop at about 10, and found STEM through 3D printing in middle school. He chose mechanical engineering as the most versatile discipline and calls his software skills rudimentary.
Method. He iterates on the most unusual component first: about 20 actuator iterations, three leg prototypes, one robot. Printing versus buying depends on the project's intent and the time involved. He sees his work as showing hobbyists what is possible rather than pushing the state of the art.
What's next. CARA is blind apart from an IMU and is teleoperated. CARA Mini will be his senior design project, with software-literate teammates for vision. He is finishing a semester early. His tools are Bambu Lab printers, which he notes have sponsored his videos, and Autodesk Inventor, which he learned with help from a FIRST Robotics mentor.
- A rope-driven reducer can replace gears without slipping. Musa, a senior mechanical engineering student at Purdue, says his quadruped robot has no gears: smooth drums with spiral grooves are coupled by tensioned rope anchored on both sides. He answers the obvious objections (slip and stretch) with the capstan equation, under which friction rises exponentially with each turn of rope, so it does not slip. The motors are conventional: brushless, driven by ODrive field-oriented-control controllers that give the joints a springy compliance.
- Iterate hardest on the most novel component. He says the actuator alone took perhaps 20 iterations, the single leg about three prototypes, and the full robot was built once. Whether to print or buy a part depends on the project's intent, such as fully DIY versus just making it work, and on time, since designing your own actuator can take months.
- His robots show what hobbyists can do at home. He calls Boston Dynamics "a bunch of guys like me" with industrial equipment, and says his robots could be made by them; the appeal of his videos is showing something similar can be built in an apartment. The robot, CARA, is blind and teleoperated apart from an IMU for stability.
- His next robot is a cheap, open-source mini version. CARA Mini, still with capstan drives, will be his senior design project and open source, with teammates from outside mechanical engineering for computer vision. He is graduating a semester early thanks to AP and summer classes, and says his aim is full-time content creation alongside his own startup.
- School teaches theory; building happens at home. Musa says his schedule is mostly lectures, labs and recitations, so his robots are where he applies what he learns, and he is motivated by being told something cannot be done. His tools are three Bambu Lab printers (he discloses they have sponsored his videos) and Autodesk Inventor, learned partly from a FIRST Robotics mentor.
Crosstalk Solutions$150 for this Fanvil Video Intercom Kit? Here's What You Need to Know
- Fanvil's entry-level video intercom kit impressed the host for its price. The kit costs $150. Crosstalk, a VoIP provider that says it does not usually resell Fanvil, installed it for a customer. The kit pairs an i60 door phone (2 MP camera, call button, RFID reader, door-strike relay, IP65, IP66 with an optional rain cover) with a 7-inch touchscreen indoor station on PoE or a power adapter and Wi-Fi; the i60K adds a keypad for a few dollars more.
- Without a PBX, the kit does little beyond call-and-unlock. The two units pair over the LAN in about a minute, but then the door phone can only call the indoor station and the station trigger the relay. Registering both as SIP extensions on a FreePBX, here a Crosstalk-hosted one, adds voicemail, outbound calling and speed dials from the indoor station, and SMS, which he did not try.
- Door voicemail uses call-forward-on-no-answer to the voicemail code. On the indoor station he set forwarding to
*513(*sends straight to voicemail in FreePBX, 513 being the station's extension); he used 10 seconds of ringing for the demo but suggests 20–30 in production. - The i60 also rings ordinary VoIP video phones. Pointed at a Crosstalk 280 phone, it delivers H.264 video once answered, though unlike the indoor station there is no preview before pickup, and
*triggers the relay. RFID unlocking worked with a key fob he had lying around; the bundled smartphone-app licences he did not test. - The camera's narrow angle and cloud-PBX streaming are the caveats. He advises mounting the i60 higher than a normal doorbell so it doesn't just show visitors' belts. Video on the indoor station stuttered through the cloud-hosted PBX, which he says disappears with a PBX on the local LAN.
Master InvestorLarry McDonald: The bottom line on tech stocks
- McDonald compares the AI capex race among hyperscalers to the US shale boom. He says institutional investors who were bullish on Meta a year ago have since flipped, likening today's overspending to the shale industry's 2010–2014 arms race, where companies overspent on capex to avoid appearing weak before eventually becoming disciplined, lower-debt, cash-generating businesses — a path he expects tech to repeat over the next decade.
Dr. Jon Krohn914: Data Lakes 101 (and Why They’re Key for AI Models) — with Oz Katz
- A data lake trades a warehouse's rigid structure for speed. In Katz's own definition, which he notes differs from vendors such as AWS or Snowflake, a data lake is essentially a shared folder where every team's streams (Salesforce or HubSpot marketing data, ERP inventory) land in one place. A data warehouse, by contrast, needs rigidly structured tables, gatekeeping specialists and expensive technology. He accepts that the lake's "inherent mess" is sometimes the point, because people can answer questions with data that isn't well formed.
- Multimodal AI turns one source of truth into "multiple sources of lies." Katz says valuable data is no longer mainly tabular: it includes images, their embeddings, labels and other modalities, often chained through pipelines (audio to text to image). One image may exist as a raw file in the lake, an embedding in a vector database, features in another database and labels in a third-party labeling tool. Those copies fall out of sync, teams overwrite data others depend on, and permissions lock out people with valid reasons for access.
- lakeFS applies Git-style version control to data. Katz, the company's co-founder and CTO, describes branching the entire organizational lake without physically copying it, changing data in isolation, and merging through pull requests that the owning team signs off. Automated checks, his examples being minimum sharpness or at least 500 pixels wide, can run before a daily ingestion job commits and merges. Because code must reference a specific data version, deterministic code on the same data reproduces the same result. The core versioning engine is open source, with a CLI, UI and SDKs.
- Data infrastructure is converging on the object store. Katz says S3 has added support in recent months for tabular data via Apache Iceberg and for use as a vector database. Iceberg, an open-source project about five or six years old, represents a schema'd, modifiable table on top of an object store that any engine (pandas, Athena, Snowflake) sees in the same state. Image labels and metadata can live there as Iceberg tables. lakeFS is betting on that object store as the organization's source of truth.
Polis Pandit$We Politicized the Apolitical BLS, and That's a Huge Problem
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Scott HanselmanScott Hanselman's TEDx: We're Going to Make Things
- A teacher presented the computer as a tool for making. Hanselman says Mrs. Marian Mayfield Hill, his fifth-grade homeroom teacher and "a Portland jazz legend", brought an Apple II in when he was about 10 and said "We're going to make things with this." It fit a family of makers, with a woodworking firefighter father and a zookeeper mother who made the leather for the zoo's birds-of-prey program, and started a 30-year love affair with tech.
Master InvestorLarry McDonald: Why commodities are surging
- McDonald says the AI buildout makes this cycle more commodity-bullish. He points to a $2 trillion AI capex binge requiring roughly another $2 trillion in power-grid and energy infrastructure, plus added demand from rebuilding Ukraine, Los Angeles, and Gaza.
- He sees a high probability of a near-term copper shortage. He likens it to an "OPEC 1970-style" shortage a year or two out, attributing it to a decade-plus of politically suppressed copper exploration colliding with rebuild-driven demand.
Stock Tok$Critical Stock Market Update + Not getting stopped out as often
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Dr. Jon KrohnHow to Get Started with Adaptive ML (feat. Adaptive CEO Julien Launay)
- Large enterprises come first because their scale feeds post-training. Launay says Adaptive, founded a bit over a year and a half before recording, focused on large enterprises because serving millions to hundreds of millions of users raises costs, which motivates moving to smaller models. That scale also generates many interactions to use as post-training data. Deployments mostly run in the customer's own infrastructure, there is no self-serve cloud yet, and access is through the sales team. He says wider availability is coming "very soon."
- Synthetic data proved strong enough to make production data optional. Launay says synthetic data had been on Adaptive's roadmap for the end of its first year, but how well it works "positively surprised" the team. He claims an 8-billion-parameter model can be brought to frontier performance on a task "mostly entirely" with synthetic data. Because anyone can define and run those pipelines without an existing user base, the company is considering wider availability, including for open-source projects.
AI Analyst LabHow Top AI Product Managers Evaluate Products | Ep36
synopsis
This episode of the Data Neighbor podcast is an interview with Aman Khan, head of product at Arize AI, an eval and observability platform. His position is that how much evaluation an AI product needs depends on its scale and its stakes: a quick local eval for a hobby app, scrappy hand-grading at around 100 users, and eval-driven development with gates and production feedback at scale. Whatever the stage, evals have to be tied to the business outcome, not just to rule-following scores.
What an AI PM is. Khan describes three overlapping roles: PMs whose core product contains a non-deterministic model, platform PMs concerned with model choice, fine-tuning, cost, latency and security, and "AI-powered" PMs who use AI tools to prototype and analyze. He expects them to merge as AI becomes as common as the database.
What an eval is. Where a lending model once retrained on whether loans defaulted, the LLM-era equivalent is a customer-support bot's thumbs up or down. Evals go further: code checks such as edit distance, and an LLM judge grading tone, correctness or rule following, aligned to a human-labelled set of about 10 to 100 examples. He accepts the "evals are just unit tests" meme only in part. Scores should rise when a change is made, but the same input can give 10 different outputs, so tests must allow for variance.
Three workflows. In the first, for apps built for yourself, you ask Claude Code or Cursor to write a local eval. In the second, at 100+ users, traces go to a spreadsheet or Airtable for thumbs-up grading and early error analysis, which he says will not scale. The third, for thousands or millions of users, is eval-driven development: evals as requirements, gates against breaking changes, A/B tests of models, and a way to catch disagreement, such as a refund correctly refused that the customer still rates badly.
Business outcomes and accountability. A host compares this to defining success criteria before launch. Khan asks what happens when the eval is good but the business metric is not, and who labels the data. His example is a data practitioner accountable for the eval metric while the PM owns the outcome. Using companion and therapist-style agents, he argues that multi-turn and session-level evals matter, and that sometimes users need to be challenged rather than simply helped.
Form factor and tools. Khan points to Claude Code as an early sign of product-market fit in a terminal interface. He cites reading that coding tools currently help less-technical people more than deep experts, and the hosts agree from their own work, including a team retro where someone said they would have been faster without AI.
Four principles and latency. Khan's framework for deciding eval effort covers the user (the CEO test, the intern-in-front-of-a-client test), the value of being right and the cost of being wrong, tolerance for waiting, and where the eval sits, from offline tests to a pre-display guardrail. On waiting, he describes a Claude research-mode query that usually takes about 5 minutes running past 15 with no indication of when it would finish. He proposes classifying a question's complexity and showing an ETA. One of his prompting experiments is to append "anticipate three more problems" so the agent keeps working.
Data teams and insight. A host says many companies are still trying to trust their data at all. Khan calls getting insight from aggregate data the hardest problem, which Arize is working on with its own analysis agent, "Alex". He also predicts that, with AI-written text everywhere, better human writing will be "the next alpha".
- AI product management splits into three overlapping roles. Aman Khan, head of product at Arize AI, names a PM whose core product contains a non-deterministic "black box", a platform PM handling model selection, fine-tuning, cost, latency and security (often the central ML team), and an "AI-powered PM" who prototypes, writes specs and analyzes data with tools such as Cursor. He predicts the circles will converge as AI becomes "as ubiquitous as the database".
- Evals resemble unit tests but must allow for variance. He describes thumbs up/down as the LLM-era ground truth, then an LLM judge grading tone, correctness or rule following, aligned against a human-labelled set of roughly 10 to 100 examples so it can be trusted on new outputs. Unlike a unit test that passes 10 of 10 times, the same input can give 10 different outputs, so tests have to be built around a probability of passing.
- Eval rigor should scale through three workflows. For a vibe-coded app for yourself or friends, he says just ask Claude Code or Cursor to "write me an eval" to run locally. At 100+ users most teams log traces to a spreadsheet or Airtable and grade by hand, which he says will not scale. At thousands or millions of users you need eval-driven development, with evals as requirements and gates, A/B tests of models, and a way to catch cases like a correctly refused refund that the customer still thumbs down.
- Evals must map to business outcomes, not local metrics. Khan's question for teams is what happens when the eval is good but the business metric does not move. He also asks who labels the eval data: if the data practitioner is accountable for the eval metric but the PM is responsible for the outcome, should the PM be labelling? A host adds that teams with established success-metric frameworks forgot them after GPT-3.5.
- Four factors decide how much eval a use case needs. Khan lists who the user is (would the CEO accept this output; would you put an intern in front of your top client), the value of getting it right and cost of getting it wrong (low for coding agents), how long users will wait (someone willing to wait 5 minutes with low tolerance for error justifies a check on every output), and where in the workflow the eval sits: offline, during development, in production, and as a guardrail before output reaches the user. He arranges these as a pyramid.
Scott HanselmanAI's Trust Problem: When Your Robot Gets Caught!
- AI discovered after the fact costs far more trust. One speaker says customers trust you less whenever you use AI rather than yourself, but trust falls much further if they find out later it was a robot, or that they were recorded. Saying up front "I use this device, for this reason" draws few objections; the other speaker compares it to a reporter putting a recorder on the table.
All Things FinancialThe Marin Paper - IEEPA - Taxing(fee) Reserve Currencies - China REM and fees 8-13-2025 Part 2
- US tariffs now average 22.5%, above the Smoot-Hawley levels the author links to the 1930s global depression. He frames this as context for the Marin paper's proposal to use presidential emergency powers rather than multilateral negotiation to weaken the dollar.
- The paper proposes an IEEPA-based "user fee" on foreign sovereign holders of Treasuries, avoiding the word "tax" to sidestep tax treaties. It would start small (1%) and escalate, withhold interest payments, hit geopolitical adversaries like China harder than allies, and leave private-investor holdings untouched — the goal being to make reserve accumulation less attractive without provoking a sell-off.
- The paper itself warns the fee could spark reserve selling, a dollar rout and a rate spike, and it requires the Fed's "voluntary cooperation." The author notes China's rare-earth-minerals leverage complicates differentiated treatment, and reads the plan's reliance on Fed deference as Trump effectively setting the yield curve, since the Treasury Secretary reports directly to him.
Scott HanselmanUnlock the Spicy Autocomplete Revolution in Coding!
- Vibe coding extends IntelliSense rather than replacing the coder. The guest rejects a "be one with the AI" reading of vibe coding and says the definition of coding will keep evolving with its tools, tracing a line from first seeing IntelliSense to IntelliCode guessing intent. Hanselman calls that "spicy autocomplete"; the guest says it is that "but++", where you define what you want to accomplish and the tool does it.
Stock Tok$A Stock To Buy & A Stock To Watch 👀
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Dr. Jon KrohnHow LLMs Are Actually Trained: Pre-Training vs. Post-Training Explained (with Julien Launay)
- Pre-training teaches next-token prediction across nearly all existing text. Launay says pre-training, historically where most compute went, trains models on "nearly every text in existence," now plus images and video, across tens or hundreds of thousands of GPUs. The result is unwieldy. Asked a question, a raw model might output 10 similar questions, because its data makes a list of questions as likely as an answer.
- Post-training uses feedback, usually RL, to shape how a model is used. Launay says post-training can be just continued training on chat transcripts, but its big success has been reinforcement learning. The model learns from a good or bad signal about its answer, from a human, another model or another source, not from explicit demonstrations. The host gives ChatGPT's thumbs up and down as one source of such data.
- The two phases are blending, and post-training spend is approaching pre-training's. Launay says pre-training now shifts its data mix, moving from bulk data toward "higher quality" code, maths and chat, and that RL is starting to appear during pre-training. Where post-training was once a small spend next to the millions or hundreds of millions of dollars that went to pre-training, he says recent work, including xAI's statements about Grok 4, reports spending nearly as much on post-training.
Scott HanselmanAugmentation Vs. Automation: Superpowers for Your Job
- Augment workers where possible rather than automate jobs. The guest argues that automation takes a depth of task analysis, covering every intricacy and dependency, that most companies are unwilling to do. Augmentation instead starts from asking someone which part of their job they hate most and giving them a "superpower"; the interviewer raised exoskeletons as an example of personal augmentation.