Souk's Notes - Unofficial Summaries
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Dr. Jon Krohn882: 40x Hotter Than the Sun: The ASML Machines That Make AI Chips (@JonKrohnLearns)
- ASML alone can build the machines behind leading-edge AI chips. The host says the Dutch firm's latest lithography system weighs 150 tons, is roughly the size of two shipping containers and costs $350 million. No other company can reliably print features below 7 nanometres, and ASML holds over 90% of the market even for mature technologies, supplying TSMC, Samsung and Intel.
- EUV light comes from tin plasma about 40 times hotter than the sun. The host describes the machine firing 50,000 droplets of molten tin into a vacuum, each hit twice by lasers, producing plasma near 220,000°C. The light is bounced off mirrors with imperfections measured in trillionths of a metre onto a chip blueprint. The resulting processors can hold over 100 billion transistors, more than 70 layers and over 100 km of wiring on silicon about 1.5 times a US postage stamp.
- Lithography leadership is measured in decades, not months. The host argues that ASML spent two decades perfecting EUV and money cannot compress that. Its high-NA EUV raises aperture from 0.33 to 0.55 to print 8 nm features, with mirrors doubling in size and growing 10 times heavier. Hyper-NA above 0.75 is next, and wavelengths around 6 nm are seen as a plan B.
- China is squeezing older tools while building its own. Barred by the US from ASML's most advanced equipment, Chinese makers use multi-patterning on older machines to print features two or four times smaller, at a cost in complexity and speed. A state-owned firm is reportedly progressing on a 28 nm-capable tool. The host says matching EUV would mean replicating ASML's supply chain of more than 5,000 specialised suppliers.
- Canon's nanoimprint bet is cheaper but not yet chip-grade. Per the host, Canon's press-like nanoimprint lithography could in theory reach nanometre accuracy at about 40% lower cost. Defects, alignment and speed have so far confined it to uses like smartphone displays and memory chips, where higher defect rates are tolerable.
Scott HanselmanNeverending support for open source with Hayden Barnes
synopsis
Barnes, open source partner manager at HeroDevs, argues that paid "never-ending support" for end-of-life open source frameworks benefits everyone. It gives enterprises and agencies a long runway to migrate, takes the support burden off upstream maintainers, and, by his account, faces demand that grows the longer a version has been dead.
How it works. A customer takes a token from the HeroDevs portal and repoints its package sources at the company's repository. From then on, high and critical CVEs in the old version are patched and arrive on normal builds. The catalog runs from AngularJS, Node, Vue and Bootstrap down to Spring and now .NET 6. Barnes puts the company at about 120 people and mostly bootstrapped.
Upstream relations. Barnes rejects the idea that HeroDevs just forks and resells. It hires project staff, sponsors foundations such as OpenJS and shares revenue. Projects then cite HeroDevs in their deprecation notices.
Why staying unpatched is risky. Speaking as a former attorney, Barnes cites CISA patching deadlines for government contractors, sector rules in finance, insurance and healthcare, and EU regulation. He also notes negligence exposure in class actions after a breach.
Scale and scope. He says growth on long-dead versions is "exponential" as mandatory CVE scanning uncovers forgotten apps. For .NET the focus is end-of-life LTS releases. Smaller libraries are evaluated customer by customer, and each is either forked or maintained under contract by its author. Packages must match each Linux distribution's build bit for bit, and a WinGet repository is planned. Hanselman, who still urges upgrading, praises HeroDevs' public vulnerability directory and its RSS feed.
- Never-ending support is a drop-in package feed, not a migration. Barnes, open source partner manager at HeroDevs, says customers get a token from the customer portal and point their package sources (package.json, Maven, Gradle) at the HeroDevs repository. From then on, high and critical CVEs in end-of-life versions are patched and pulled in on normal builds under the company's SLA. He describes HeroDevs as about 120 employees and mostly bootstrapped, helped by Google effectively blessing it as AngularJS's maintainer when that was deprecated.
- Paid support for old versions funds and relieves upstream maintainers. Barnes says HeroDevs hires key project staff, sponsors bodies such as OpenJS, funds community efforts and shares revenue upstream. In his account, projects can then point to HeroDevs in their deprecation notices and keep innovating on new versions. Hanselman frames it as a balance between open source and capitalism, though he says he will keep urging people to upgrade.
- Running unpatched software carries real regulatory and legal exposure. Barnes, a former practicing attorney, cites CISA rules requiring government contractors to patch vulnerabilities within set times, extra requirements in insurance, financial services and healthcare, and EU regulation. He adds that a breach traced to an unpatched, remotely exploitable high-severity CVE helps show negligence in a class action.
- Demand for end-of-life versions grows the longer they're dead. Barnes calls the growth "exponential", because companies and agencies rolling out mandatory CVE scanning keep finding forgotten apps; large .NET customers run tens to hundreds of applications. The current .NET focus is LTS releases that have gone end of life, starting with .NET 6, which ended support in November 2024. An STS version would be considered on demand, since the catalog is customer-request driven.
- Long-tail libraries are triaged per customer. At onboarding, HeroDevs reviews each dependency and decides whether to fork it internally or pay its maintainer to keep a branch, drawing on research into the support life cycles of the top 200 community NuGet packages. Builds must match distribution packaging bit for bit for apt and RPM repos, with a WinGet repo planned.
Crosstalk SolutionsCrash Course! Complete FortiGate Setup Guide for Small Business
- Size a FortiGate by its threat-protection throughput, not its ports. UTM inspection is CPU- and RAM-heavy: the FortiGate 70F lists over 1 Gbps of IPS and NGFW throughput but only 800 Mbps of threat protection. The host, from a firm that says it is a Fortinet partner, chose the 70F as about the smallest model supporting explicit proxy and SSL decryption.
- Several features are hidden from the menu by default. The 70F ships at 192.168.1.99 on port 1 with user admin and no password; options missing from a guide are under System > Feature Visibility. The host moves the LAN to 10.10.0.1 and adds employee (VLAN 20) and guest VLANs, tagged down to a UniFi switch as a third-party gateway network.
- A WAN cannot join an SD-WAN group while anything references it. The host deletes the factory LAN-to-WAN policy first, and configures WAN 2 even if absent so failover is plug-in later. His SLA pings Cloudflare and Quad9 every 500 ms, marking a link down after five failures and up after five successes; the rule prefers WAN 1 over WAN 2.
- Security profiles only act on the firewall policies they are enabled on. FortiGate denies everything not explicitly allowed, including inter-VLAN traffic; LAN-to-WAN rules need NAT, LAN-to-LAN rules should not have it. The host writes one rule per network so each can carry its own antivirus, web and DNS filter profiles, such as separate student and teacher filters.
- SSL inspection needs the FortiGate's CA certificate deployed to clients. Without it, every HTTPS page shows a certificate warning; pushed through something like Microsoft Intune, it is invisible. Without decryption, the host says, filtering still works but is less effective, since malware over HTTPS cannot be seen; the default antivirus profile blocks rather than monitors.
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Dr. Jon KrohnWant to Run PyTorch and JAX on AI Chips? Use AWS Neuron (with Emily Webber)
- NxD hides most of the Neuron SDK's complexity from developers. Webber says the Neuron SDK spans low-level runtime, driver and compiler up to NeuronX Distributed (NxD), the main modelling library. NxD compiles a model and shards a checkpoint such as Llama or PixArt across an instance's accelerators, handling communication, optimizer updates and the forward pass. Prebuilt NxD model packages let users run alignment, supervised fine-tuning or hosting experiments with little exposed complexity.
AI Analyst LabShe was the First Data Hire at a Startup - 3 Things That Shocked Her | Ep24
synopsis
Jesse Lee, former head of data at Descript and before that a product data science lead at Meta, tells the Data Neighbor podcast hosts what it took to build a data function from nothing as a startup's first data hire. Her answer is that best practices still work, but only once you have mapped where the company actually is, and that the hardest skill is calibrating your own judgment when nobody above you knows more. After three-plus years, she says she got what she went for: a lean, effective stack, real ownership of a function, and a close view of how a CEO operates.
Why she moved. She frames the decision as push and pull. The pull was wanting to understand the full data lifecycle in order to lead data someday, earlier startup enjoyment at Flipboard, a husband who is a founder, and a one-year-old whose costs were still years off. The push was Meta's zero-to-one work, where sign-off meetings consumed the team and six-month performance cycles demanded incremental value before product-market fit. She uses the same lens on career advice: list your dimensions before interviewing, collect signal throughout, and score the offers. For people who just need a job, think two jobs ahead.
Day one. The company made what she calls an opportunistic hire: people cared about data, but there was no vision or roadmap. Her biggest shock was that she could not simply import Meta's data culture without understanding local pain points. Her onboarding was mostly learning the product. She found video editing far more complex than the social and marketplace flows she knew, and the company ran a two-week "creator boot camp". She built the map herself, using a large table of expectations, status quo and gap across data foundation, insights and culture.
Building the function. Foundation came first because of garbage in, garbage out, so about three months in she hired a founding analytics engineer and taught herself dbt and data modeling. She built experimentation from scratch by comparing vendors' documentation and statistics, and says that taught her more than Meta's polished platform did. The resulting stack includes Fivetran, dbt and Hex notebooks. Hiring was intense, at times 12 to 14 interviews a week. She looks for self-starters who are resourceful, at ease with ambiguity and growth-minded, and she says candidates who rank work-life balance first should reconsider startups.
Balancing the urgent and the important. Quarterly, the team targets about 50% big-rock projects, 30% operational work and 10–20% culture and side work. Day to day, she uses decision-driven triage: she estimates about 40% of requests fall away when stakeholders are asked what they would do differently, and the rest get scoped down.
What was hardest. Being the ceiling of her function meant no internal calibration, and outside heads of data had different contexts. She says she overcorrected into humility and found it disorienting. Asked about underrated skills, she points to AI-native workflows, saying her team had used AI only to enhance existing work.
- Leave big tech for a startup with explicit push and pull reasons. Jesse Lee, former head of data at Descript, says the pull was learning the whole data lifecycle on the way to leading a data function, plus a window of low financial pressure while her child was one. The push was Meta, where zero-to-one products carried heavy sign-off processes and six-month performance cycles that demanded incremental value before product-market fit. She contrasts this with Figma's six years in stealth.
- Score job offers against dimensions you listed before interviewing. Lee advises collecting signal on each dimension from the first interview onward and scoring offers by weighted or simple average. For people who just need a job, she passes on advice to think two jobs ahead and to treat the current employer as paying you to build the skills you want.
- Imported best practices fail unless you meet the company where it is. Lee says her biggest shock was that there was no data culture, no vision and no roadmap, and that bringing in big-tech practice wholesale met resistance. She organizes the work in three buckets: data foundation, data insights (strategic and operational), and data team and culture. Her emphasis shifted from foundation, where she hired a founding analytics engineer about three months in, toward insights and culture. In hindsight she would have hired a data or analytics engineer first.
- Split capacity by fixed shares and triage requests by decision. Each quarter her team aims for about 50% on "big rock" projects, 30% on operational asks, and 10–20% on side projects, a "data university" and brown bags. Day to day, she asks stakeholders what decision the data would change. She estimates about 40% of requests can be pushed back, and many of the rest end once a scoped-down version is delivered.
- Startup fit means self-starting and calibrating your own expertise. Lee lists autonomy, resourcefulness, comfort with ambiguity and a growth mindset. She says work-life balance as a top priority is a poor fit, though she probes rather than rejecting anyone who asks about it. As the "ceiling" of her own function, she found the hardest part was judging which past practices still applied. She says she was overly humble, became disoriented, and learned by deliberately reading experimentation vendors' documentation and taking dbt and data-engineering courses.
Crosstalk SolutionsControl Pi-hole on Your Apple Watch!
- A wrongly blocked domain is fixed by allowlisting or pausing Pi-hole. The host meets this most during online shopping; a temporary disable resumes blocking automatically when the timeout expires, but normally needs the Pi-hole web interface.
- Pi-hole Remote's paid version controls blocking from an Apple Watch. The free iPhone app connects to multiple Pi-hole instances and can disable blocking; the paid version, which cost the host $9.99 for a lifetime licence, adds the watch, with pauses from 1 minute to 8 hours, for the rest of the day, or indefinitely.
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Dr. Jon KrohnWhat Is a Kernel? How AI Chips Really Run Your Code (with Emily Webber)
- A kernel is where the user overrides the compiler on the chip. Webber explains that PyTorch code for Trainium and Inferentia passes through PyTorch XLA into an HLO graph, which AWS's compiler turns into the chip's instruction set. A kernel replaces that generated code with operations the user defines through NKI, the Neuron Kernel Interface.
- Kernel work is the study of compute optimisation for a workload. Kernels range from a hello-world tensor add to a full forward or backward pass or just an MLP projection, Webber says, and writing one means studying data movement, memory and compute utilisation to improve performance on large language models.
Dr. Jon Krohn881: Beyond GPUs: The Power of Custom AI Accelerators — with Emily Webber
synopsis
Emily Webber, of AWS's Trainium and Inferentia team, makes the case that infrastructure decides who wins with foundation models, and that AWS's own accelerators are a serious alternative to GPUs on price-performance. Her walk-through runs from how PyTorch code reaches the chip, through the jump from Trn1 to Trn2 and the UltraServer, to the tuning choices and customers behind it, and ends on her view that models will absorb ever more knowledge into their weights while hardware keeps advancing.
From software to hardware. Webber, who came to computer science late via finance and public policy, joined Amazon as one of SageMaker's first solutions architects and built distributed-training capabilities that led to SageMaker HyperPod. She moved to hardware once she concluded that accelerator supply, size, health and efficiency decided customers' success.
How code reaches the chip. PyTorch models are compiled through PyTorch XLA into an HLO graph, which AWS's compiler converts into the hardware's instruction set. A kernel is where the user overrides that process and defines on-chip operations directly through NKI, the Neuron Kernel Interface, from a trivial tensor add to a full forward pass or an MLP projection, studying data movement and memory and compute utilisation. Above that sits the Neuron SDK, whose main modelling library, NeuronX Distributed, handles compiling and sharding a checkpoint such as Llama across an instance's accelerators. Trainium also runs SageMaker Studio and serves models through SageMaker JumpStart.
Why not a GPU. Webber frames AWS's stance as customer choice over data, models and hardware. She tells the story of Annapurna Labs, acquired in 2015: its Nitro system physically separates customer data from instance control and underlies every modern EC2 instance; its Graviton Arm CPUs now account for over half of new AWS compute, per re:Invent; Trainium and Inferentia are its third line. Customers come, she says, for price-performance and energy efficiency, and she claims Trn2 is AWS's most powerful AI/ML instance.
Trn1 to Trn2. The NeuronCore is essentially unchanged, so kernels and NxD code carry over. Trn2 has four times the compute: eight cores and four HBM banks per card instead of two and two, 96 GB per card, 16 cards and 1.5 TB per instance. An UltraServer links four Trn2 instances, 64 cards across two racks, over NeuronLink with at most two hops between cards. Asked how big a model fits, she declines to give a maximum, since headroom is needed for batches, optimizer state and replicas, and instead recommends Trn1 for models around 70 billion parameters and Trn2 beyond that.
Tuning the distribution. When the host takes TP to mean numeric precision, Webber corrects him: it is tensor-parallel degree, the number of cores sharing one tensor copy. Raising it changes time spent in collectives such as all-reduce, the batch size that fits and step time, and can improve or degrade performance, so NxD exposes it as one parameter. Trn2's logical NeuronCore setting groups cores in pairs, cutting 128 visible devices to 64 with more HBM bandwidth each.
Choosing instances and customers. Trainium's 4D torus suits backward passes; Inferentia's simpler layout suits forward passes and comes in more sizes. Anthropic is the flagship customer, with Project Rainier a very large Trainium cluster; Databricks and the host's mention of Poolside follow. Build on Trainium offers academics $110 million in credits on up to 40,000 Trn1 cards.
What comes next. Webber argues knowledge should be pushed into the network wherever possible, calling RAG and agentic layers "fluff" beside the weights, and says Trainium 3, pre-announced at re:Invent, is on the way.
- Customers pick AWS's own accelerators mainly for price-performance. Webber, who moved from SageMaker to the Trainium and Inferentia team because she saw accelerator supply and health become "make or break", frames the chips as part of AWS offering hardware choice. She traces them to Annapurna Labs, acquired in 2015, whose Nitro system and Arm-based Graviton CPUs (over half of new AWS compute, per re:Invent) preceded Trainium, and claims Trn2 is AWS's most powerful AI/ML instance.
- The Neuron software path lets PyTorch code reach the chip, with kernels as an override. Webber explains that PyTorch XLA turns a model into an HLO graph that AWS's compiler lowers to the chip's instruction set, while a kernel written in NKI, the Neuron Kernel Interface, replaces compiler output with user-defined operations tuned for data movement and utilisation. NeuronX Distributed (NxD) handles compiling and sharding checkpoints across accelerators, and Trainium also runs under SageMaker Studio and JumpStart.
- Trn2 quadruples Trn1's compute with the same NeuronCore. Webber gives Trn2 eight NeuronCores and four HBM banks per card versus two and two, 96 GB of HBM per card, 16 cards and 1.5 TB per instance. An UltraServer joins four Trn2 instances, 64 cards over two racks, via NeuronLink with at most two hops card to card. Because the core is unchanged, kernels and NxD code move up easily; she recommends Trn1 for models around 70 billion parameters and Trn2 for much larger ones.
- Tensor-parallel degree is a tuning knob with real costs. Webber corrects the host, who took TP for numeric precision: it is the number of NeuronCores sharing one copy of a tensor. Changing it, from TP8 on Trn1 to TP32–128 on Trn2, shifts time spent in collectives such as all-reduce, batch size and step time, and can help or hurt. Trn2's logical NeuronCore setting regroups 128 visible cores into 64 to give each more HBM bandwidth.
- Anthropic is the flagship customer, and academics get $110 million in credits. Webber cites Project Rainier, a very large Trainium cluster built with Anthropic, plus small-language-model startups and Databricks; the host adds Poolside. Build on Trainium offers $110 million in credits to academic researchers, scaling projects on a cluster of up to 40,000 Trn1 cards, and recently paid $25,000 for the fastest Llama implementation in NKI.
Polis Pandit$Due Process Is Fundamental, and Why We Need Podcast Standards Along With a Trump Bond Tracker
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Dr. Jon KrohnThe Hidden Risks of LLMs Without Internet Access (with Greg Michaelson)
- A model without web access may silently invent what a link says. The host recounts giving OpenAI's o1 pro a URL in deep research, at a time it lacked internet access; its reasoning trace said it would assume what the page contained, and he says the output alone would have misled him had he not checked the trace or the site.
- LLMs graded each other erratically but showed no self-preference. Michaelson describes generating hundreds of SAT essay questions, having four LLMs answer them, then having the same four rank the answers. Results were "wildly unstable" between reruns, but he saw no sign of a model favouring its own answers.
- LLM committees are his answer to evaluation bias. Michaelson still finds LLMs effective at judging LLM output, and suggests playing models off one another, for instance letting a cheap model screen answers and calling an expensive one only when an answer looks questionable. He mentions teacher-student distillation from a recent AWS talk in Ireland but says he hasn't looked into its specifics.
Dr. Jon KrohnThe LLM Revolution Threatens SaaS Businesses (with Greg Michaelson)
- This upload holds a different clip than its title names. The video posted as "The LLM Revolution Threatens SaaS Businesses" is a copy of the 2025-04-19 clip on deploying models: both run 5:56 and their captions are identical. Greg Michaelson's SaaS argument is summarized under the 2025-04-17 clip and episode 879, dated 2025-04-15.
Dr. Jon KrohnNo More Handoffs: Data Scientists Can Deploy Too (with Greg Michaelson)
- Handing models to engineers is a risky dependency, not just a bottleneck. Michaelson argues that the developers who recode data scientists' prototypes usually aren't data scientists and can easily introduce mistakes, while data scientists often lack the skills or infrastructure privileges to serialise, dockerise and deploy models themselves.
- Zerve packages environments and models so data scientists can deploy. Michaelson says every Zerve canvas runs in its own reusable, shareable Docker container, ending dependency mismatches for new hires, and every executed block serialises its variables. An API route can point directly at a trained random forest, and deployments run on Lambda-style serverless infrastructure with no long-running services. Where organisations still forbid data scientists from deploying, a handoff system passes deployable software rather than prototypes to other teams.
Crosstalk SolutionsBut...Does It AI? Minisforum AI X1 Pro overview
- The NPU stayed idle until a Windows Insider build was installed. After that, the host saw it working on live captions for a YouTube video and in AMD's Amuse image generator running the SDXL Turbo Stable Diffusion model with Ryzen AI support.
- The NPU's gain for local AI is efficiency, not speed. The host says it does not necessarily run LLMs faster than a GPU but draws less power and heat; he expects real NPU support in tools like Ollama within a few updates, though for now it takes manual setup.
- The host rates it his likely favourite mini PC as a workstation. Testing since early February, he saw no slowdowns from the 12-core Ryzen AI 9 HX 370 with Radeon 890M graphics, but not for competitive gaming. It has dual 2.5 GbE, Wi-Fi 7, OCuLink and an internal power supply, from $929 with 32 GB and 1 TB, up to 96 GB and 2 TB.
Dr. Jon Krohn880: Manus, DeepSeek and China’s AI Boom — with Jon Krohn (@JonKrohnLearns)
- DeepSeek set off an AI mania in Chinese markets and enterprises. The host reports that the Manus agent's registration site crashed within hours of its March 6 launch, with codes reportedly resold on the black market. The Hang Seng Tech Index rose more than 40% from mid-January, and hundreds of large firms, Tencent, city governments, hospitals and universities announced plans to integrate DeepSeek's models.
- Chinese investors are funding startups while calling it a bubble. One Hangzhou-based investor, quoted by the host, said there was "no choice" given the weak economy, with a strategy of entering at an A round and exiting at an A+ round a few months later. On March 6 the central government announced a venture fund of a trillion yuan, about $140 billion, for tech investment.
- Big tech is betting cloud AI will offset weak core businesses. Per the host, Alibaba declared human-like AGI its main objective, released a reasoning model it claims matches DeepSeek, and committed around $53 billion over three years to data centres, more than in the past decade, from a 36% share of China's cloud market. Baidu's cloud revenue has risen.
- The boom is driving a server and hardware spending surge. Demand for AI servers has soared since early February, including all-in-one on-premises units favoured by state-owned firms. A server maker founded by former Huawei staff is up about 140% in 2025, and Jefferies analysts estimate server makers may spend over 1.4 trillion yuan, about $200 billion, over two years.
- Chip access is the boom's chief vulnerability. Morningstar's Kai Wang argues DeepSeek won't fundamentally change most rally beneficiaries, and the host notes an earlier rally faded. Chinese firms still buy Nvidia's weaker H20, local designers such as Cambricon and Huawei lag, and SMIC is capacity-constrained. The host warns that reported US plans to restrict even the H20 could push costs back up and end the euphoria, while welcoming the open-sourcing DeepSeek leads.
Technology and FriendsDamian Synadinos on Trust [EPISODE 850]
- Trust rests mostly on faith, but not only on faith. Synadinos says most definitions of trust involve faith or belief. Between blind faith and hard evidence he puts familiarity (an unbroken history of earlier trust) and authority (a boss or someone held in high regard).
- Risk decides whether trust is enough. Where the outcome could be catastrophic, or something bad is very likely, he says trust may be insufficient and actions should rest on more than faith. Where even the worst case is a "nothing burger", or very unlikely, trust is fine. The host offers his own pair of examples: lending someone a dollar against running an unknown executable that could compromise his network.
- Gathering evidence is costly and sometimes impossible. It takes effort, time and maybe money. Synadinos's example is bridges and elevators: failure could kill you, but you cannot interview their engineers, so you rely on faith. Trying to verify everything would mean analysis paralysis, so he calls trust a heuristic that lets the brain reach sensible conclusions without enough evidence.
- Skepticism is also wrong when misapplied. His flowchart, which he says is the slide audiences like best: pressing questions on a low-risk matter reads as cynical or jaded, while failing to question a high-risk one, such as plugging in an unknown flash drive, leaves you naive and easily manipulated. He says he gives people tools for striking the balance case by case rather than an answer.
- He built the "Trust but Verify" talk around misinformation and AI. He says he is despondent at seeing family and friends fooled, over-trusting some sources and under-trusting others. He moved the topic into a technology conference keynote by applying it to AI, which yields a mix of true and false information. Giard, who works for Microsoft, says the "Copilot" name is apt because it keeps the human as pilot. Synadinos closes his talk with a line from Wes Moore: "I want skepticism to be your companion and not your captor."
Scott HanselmanComputer Science for Kids with Jen Looper
synopsis
Looper presents Computer Science for Kids: A Storytelling Approach (Wiley), a curriculum for US grades 6 through 8 that is mapped to standards. She ends on an open question: she cannot yet say how AI should enter teaching, and she has paused her own university course until she can. Her preference meanwhile is for fundamentals done by hand, with AI kept to the tedium.
Who it is for. Middle school is where, by Looper's research, girls in particular drop out of computer science. The book's hook is to see yourself as a builder rather than a lone coder in a basement. Readers walk through a mystical forest with a guide and solve problems along the way, such as securing a gate against a thief of rare moss. A sustainability curriculum is hidden underneath. Each chapter maps to a Computer Science Teachers Association standard. Looper says those standards are strict and expects them to be revised toward AI.
How it is built. The book moves from low-code to Microsoft MakeCode and micro:bit, then Twine, then 3D worlds in A-Frame. All of it can be done in a browser, because she wants access not to be a barrier. Teachers get a password-protected answer site and a Common Cartridge package for learning management systems. Hanselman discloses that he works with the MakeCode team.
Testing it. Looper went to her local school in Wellesley, where computer science teaching is often handed to math teachers, and then to a focus group of sixth graders. They approved the art but asked for video and quests. Consuming versus generating. Hanselman frames the book as getting kids to generate rather than consume. Looper describes first graders obliged to do hours of iPad work while she was substitute teaching. She argues for tactile, bespoke making and says she dislikes AI art. The book's illustrations are by a student artist in Venezuela.
Where AI fits. Looper has paused CS 601 at Boston University's Metropolitan College. She would teach HTML by hand with Copilot off, and have advanced students code-review a site an agent scaffolds. She relays an educator's account of a student passing off chatbot work, and a colleague pressed to grade essays with ChatGPT. In her own coding she uses Copilot for upgrades. She calls a fully agentic tool "terrifying" and sloppy, and finds Bolt.new clean.
- The book aims to keep middle schoolers by casting them as builders. Looper's Computer Science for Kids: A Storytelling Approach (Wiley) targets US grades 6 through 8, ages 11 to 13, the point where she says her research shows girls in particular drop out of computer science. Readers follow a guide through a mystical forest solving problems such as securing a gate against a moss thief, with a hidden curriculum on sustainability.
- Every chapter is mapped to a formal curriculum standard. The standards are the Computer Science Teachers Association's. Looper says they are demanding, down to compliance for sixth to eighth graders and training machine learning models from scratch by high school, and expects them to be revised toward AI this year and next. Hanselman singles out the mapping as what separates the book from whimsical kids' titles, along with its recurring "think like a computer scientist" sections on problem solving.
- Everything runs in a browser, so cost isn't a barrier. The progression runs from low-code to Microsoft MakeCode and micro:bit, which has an emulator, then Twine and finally 3D worlds in A-Frame. Looper's reasoning is that "there is ability everywhere, but access is quite limited". Teachers get a password-protected site with answers and a Common Cartridge package for learning management systems such as Canvas. Hanselman says he works with the MakeCode team.
- Local testing showed the textbook format is the weak point. Looper took the idea to the computer science teacher at her local school in Wellesley, Massachusetts, where she says computer science is often staffed from the math department, and then to a focus group of sixth graders. The students asked for video and quests, which she has begun adding on the companion website.
- Looper hasn't settled how AI belongs in teaching. She has paused CS 601, her front-end course at Boston University's Metropolitan College, while she works it out. She favours writing HTML by hand with Copilot off, and for advanced students, code-reviewing a site an agent scaffolds. In her own work she found Copilot fast for a Vue 2 to Vue 3 upgrade and Bolt.new clean, but found a fully agentic tool "terrifying" and its code sloppy. She wants Copilot as a junior engineer for toil, with the creative work kept human.
Crosstalk SolutionsUbiquiti Wave Antennas: Which Is the Right Choice for You?
- Every Wave unit backs its 60 GHz link with 5 GHz. 60 GHz is sensitive to weather and alignment, so the 5 GHz radio takes over if it drops; the host expects about 1 Gbps or more on 60 GHz but 200–400 Mbps on the backup.
- Point-to-point range and price climb together across four models. Line-of-sight ranges: Wave Pico $179, about 1.3 km; Wave Nano $279, 5 km; Wave LR $379, 8 km; Wave Pro $599, about 15 km. A link needs a pair.
- The two point-to-multipoint APs trade beam width for reach. The Wave AP Micro and Wave AP are both $499; the Micro's 90° beam covers wider, while the Wave AP's 30° beam reaches stations much further away.
Dr. Jon KrohnFull-Stack Data Science? Zerve Makes It Happen (with Greg Michaelson)
- Zerve pitches itself as a full-stack environment for the data science life cycle. Michaelson says that apart from hardware and data warehousing, which it connects to, Zerve covers data connection, exploration, visualisation, reports, GPU model training, self-built or SageMaker deployment and scheduled jobs. SQL, R and Python users work side by side, syncing to GitHub or Bitbucket so engineers can stay in VS Code.
- LLMs have made building so cheap that bespoke SaaS faces an existential crisis. Michaelson argues the old build-versus-buy trade-off has shifted sharply toward building, threatening SaaS vendors that sell custom use-case solutions "and then charging a mint for them", with knock-on effects for venture capital. He doesn't predict what the space will look like in a few years.
AI Analyst LabWhat if AI Teaches You AI | Ep23
synopsis
Jess Haberman, director of product content at Anaconda and formerly an editor at O'Reilly Media, argues on the Data Neighbor podcast that AI's main effect on technical education so far is pace. Learners move faster, expect to be taught through assistants, and outgrow long fixed curricula. She concludes that the lasting advantages are human ones: a trusted voice in a flood of AI-generated content, a clear learning goal, a network, and communication and collaboration skills that AI amplifies rather than replaces.
What has already changed. Haberman says business leaders expect AI efficiency perhaps sooner than it will arrive, but learning is visibly faster. People can learn Python as they go, with generative tools translating their thoughts into code and code back into explanation. On Anaconda Learning, she reports, users finish Python material faster and want advanced content the platform has yet to build. She also reports that most Anaconda AI-assistant use, roughly 85% by her loose figure, is for learning and exploration. The job for content teams, in her view, is to make sure the assistant teaches "the Anaconda way" and that content is ready for assistants as well as search. She mentions Anaconda's recent move to enterprise Claude and its shared projects. She treats prompting as a new skill that no one has fully mastered.
The fate of curricula. She sees microlearning accelerating, with long courses broken into transcripts, audio, cheat sheets and mini-books for different learners. She doubts bootcamps survive in their current form and gives certifications little weight in her hiring, though she notes that technical managers may differ. Academic curricula, she says, will change slowly. The academia-industry gap, especially dirty data, will remain. Charismatic, trusted instructors will keep their edge over AI ones.
Trust in a sea of slop. With AI-generated and counterfeit content flooding places like Amazon, Haberman tells authors and trainers to build a personal brand or align with an established one, and calls this a hard time to self-publish. For learners, her method is to give a chatbot your level, goal and field, ask for sources with links and dates, skip content over two or three years old, and then follow the named experts and their podcasts. She stresses real, reciprocal relationships and not fearing being wrong in public. The hosts add that posting wrong answers on forums reliably draws corrections. A host also draws out the point that learners need a destination, not endless consumption.
Her path and the wider field. An English major who had intended to be a book editor, she moved from outdoor and lifestyle publishing to O'Reilly's data-engineering list knowing little, and learned by asking conference speakers to explain things simply. She then joined Anaconda through Peter Wang. She sees finance as a ripe area given its Python literacy and notes Anaconda's Python-in-Excel partnership with Microsoft. She calls herself more hopeful than afraid about lightly regulated AI.
Evergreen skills. Communication and collaboration come first. Everyone can now write with AI help but must read the output, and well-paid specialists should show value. She also says not to pigeonhole yourself, and not to gatekeep. A host adds that people with soft skills now face a lower barrier to technical ones. Her single upskilling hack is networking.
- AI mainly speeds up learning, and learners now expect it through assistants. Jess Haberman, director of product content at Anaconda, reports that Anaconda Learning users get through its Python instruction faster and are asking for intermediate and advanced content the two-year-old platform has not built yet. She puts the share of Anaconda AI-assistant users who use it for learning and exploration at roughly 85%, saying not to quote her exactly. Her inference is that content must be "assistant friendly" as well as SEO-friendly, and built on original expert material.
- Weeks-long courses are giving way to modular content in many formats. Haberman is skeptical bootcamps will survive, and says she does not care about certifications when hiring, while noting that she does not hire highly technical roles. She expects academic curricula to change slowly and the gap with messy industry data to persist. Engaging, trusted human instructors, she argues, will still outperform AI instructors.
- AI-generated slop makes a trusted brand the differentiator. She cites AI-made counterfeit copies of bestselling books on Amazon that confuse buyers. Her advice is that trainers and authors build a strong personal brand or align with one such as O'Reilly, and she calls the present a very hard time to self-publish.
- Find good sources by stating your goal and demanding provenance. Her method is to tell a chatbot your level, goal and target field, ask for its top five recommendations with links, and skip anything over two or three years old. She also asks it for the field's top experts, then follows them, their podcasts and their guests. She says to build give-and-take relationships rather than cold-asking influencers for course lists, and notes that posting something wrong reliably draws corrections.
- Communication and cross-functional range are the durable skills. Haberman says AI removes the excuse for poor writing but must be read before it is sent. She adds that experts paid well should show value rather than offload trivia, and that cross-functional people survive a shrinking market. A host adds that strong soft skills now come with a lower barrier to technical skills. Her own upskilling hack is networking, including a managers' group that compiled learning resources for their teams.
Scott HanselmanEPISODE 14 - Scott & Mark Learn To… ShaderGlass and Vibe Coding
- ShaderGlass overlays retro shaders on any window, invisible to capture. Hanselman demos the app, written by a self-described retrocoder whose name the captions garble, applying effects such as a Game Boy, NTSC VCR or CRT-geometry look over VS Code. Neither the podcast screen share nor a screenshot could see it, so Mark watched from his iPhone. Mark explains that it writes directly to the frame buffer, so Windows is unaware of it. Hanselman calls it "100% useless" and delightful: people should build things "for the heck of it".
- Windows Terminal compiles HLSL pixel shaders dynamically. Hanselman points the experimental pixel shader path in Terminal's default profile settings at readable HLSL files from GitHub, editing parameters such as tint colour, bloom and scan lines. He says he uses a CRT shader when talking to his PDP-11.
- Vibe-coding a fractal shader failed first, then worked. Hanselman gave Claude 3.7 thinking the Dawn shader as a base. The result failed to compile ("incorrect number of arguments to numeric type") and the session broke, and he concedes he is only at the hello-world level of writing shaders. A restart produced a working dynamic-zoom fractal shader, which he published by dragging the file into a GitHub repo and having Claude write the README, which they call shovelware.
- Copilot's code citations surface licensing questions AI doesn't weigh. The output was flagged as matching code under four license types. Mark notes that many hits were boilerplate every Terminal shader contains, and that copied "magic numbers" trace back to whoever first conceived them. One source was a 9-year-old GLSL file with no license; a function traced to a 2013 Stack Overflow answer under a "do what you want" licence, which Hanselman notes is not OSI-approved but is compatible.
- Hanselman claims AI-generated code can't be copyrighted. He says the US Copyright Office ruled that AI-generated content cannot be copyrighted, so the shader is free by definition. Only lines they modify themselves would be copyrighted, which he calls "interesting territory" for AI-assisted coding. Mark first asks whether the ruling covered only generated art.
GeoHussarApril 16, 2025
- The Xia dynasty, China's claimed first, most likely never existed. The claim that Chinese civilization is older than almost any other rests on the Xia, dated by some sources to over 4,500 years ago and said to be founded by Yu the Great. The speaker reads Yu's legend — draining a great flood, making rule hereditary, founding the first dynasty — as resembling Greek or Egyptian mythology rather than history.
- Chinese scholars themselves doubt the Xia, he says. The first he cites is Gu Jiegang, founder of the "Doubting Antiquity" school of Chinese thought; the clip cuts off before his argument is given.
Dr. Jon KrohnHow Zerve Cuts Data Science Cycles by Up to 10x (with Greg Michaelson)
- Serverless blocks make parallel execution free of extra code or cost. Michaelson says Zerve's new "fleet" feature, launching the week of recording, fans code out across serverless compute, so a thousand slow LLM calls can run at once instead of in series. Because each block in Zerve's DAG already spins up its own serverless compute, parallelising needs no pool management and, he says, costs no more since total compute is the same.
- A shared DAG canvas does for notebooks what Google Docs did for documents. Code lives in the graph's nodes and data frames flow along its edges, with forks and merges, Michaelson explains, so several people can run Python, R and SQL blocks in one canvas simultaneously. He contrasts Jupyter, where metadata changes cause merge conflicts and "final final" file names; the host supplies the Google Docs analogy.
- Low-code tools hit a ceiling, so value comes from people who code. Michaelson, who spent about seven years at DataRobot, says the emperor of 2010s low-code/no-code "has no clothes", since complex problems exceed such tools, which is why DataRobot and Databricks added notebooks. Zerve, he says, remains code-first, though LLMs have turned coding into describing what you want and then tweaking the result. The host relays Zerve's claim of cutting cycle times by up to nine times; the clip has no evidence for the figure.