Souk's Notes - Unofficial Summaries

2025-04-15 · open the original ↗

• Cheap in-house building with LLMs threatens many SaaS vendors. Michaelson argues the build-versus-buy calculation has shifted so far that firms selling custom use-case software face an existential crisis, though nobody will rebuild something like Salesforce, and he separates "dime a dozen" generative AI bolt-ons from deeply integrated agents. The host sets this against Klarna's and Satya Nadella's similar views and Zoho's CEO's counter-argument that AI will spawn new vertical SaaS, and notes a friend who cut a $20-a-month ChatGPT Plus cost to $2 by calling the API through his own interface.
• Zerve's serverless blocks make parallelism and collaboration nearly free. Michaelson explains that code lives in the nodes of a DAG canvas, with data frames passing along its edges, and each block spins up its own serverless compute. That lets several people run Python, R and SQL in one canvas at once, and lets the new "fleet" feature fan out, say, a thousand slow LLM calls with no extra code and, he says, no extra cost.
• Zerve lets companies choose where prompts go to protect their IP. Michaelson says OpenAI's models are the best in his experience, though he distrusts LLM benchmarks, and sending prompts there is unavoidable with them. Zerve also offers AWS Bedrock and self-hosted Hugging Face models, which can be fine-tuned, and Zerve itself is self-hosted. Beyond prompt-driven GenAI blocks, its AI assistant is an agent that plans, creates and connects blocks from a natural-language request.
• Data scientists should ship software, not prototypes for engineers to recode. Michaelson calls the handoff to developers a risky dependency, since they may misunderstand the model. Zerve gives each canvas a Docker container and serialises every block's variables, so an API route can point straight at a trained random forest and deploy on Lambda-style serverless compute, with a handoff path where data scientists aren't allowed to deploy.
• LLM committees are his answer to bias and error in AI judgment. In Michaelson's test, four LLMs answered and then ranked responses to about a hundred SAT essay questions; rankings were "wildly unstable" across reruns, but no model favoured its own answers. He suggests cheap models screening answers and escalating doubtful ones to expensive models, and warns that a model that gets a problem badly wrong first time is rarely correctable.

2025-04-14 · open the original ↗

• Dell estimates its toolkit cuts NPU app delivery from six months to six weeks. Gupta says that by Dell's estimate a typical, not very complex AI PC app such as a RAG chatbot takes about six months from discovery to fleet deployment, covering model and device selection, runtime compatibility, on-device model operations and manual deployment. Dell Pro AI Studio, in early access at recording and due fully mid-2025, would bring that under six weeks, about a 75% reduction, leaving developers to spend time on picking the use case and validating output accuracy.

2025-04-13 · open the original ↗

• Today's NPUs comfortably run quantised models of about 8 billion parameters. Gupta puts acceptable on-device output at around 15 to 20 tokens per second, which he says rules out going much beyond an 8 or 9 billion parameter model with quantisation. NPUs passing 40 TOPS and smaller LLMs growing more capable are, in his view, the inflection that makes local inference practical.
• Dell-validated models end trial-and-error pairing of models and machines. Gupta describes developers otherwise iterating through 14 and 30 billion parameter models and memory upgrades only to land at two tokens per second, a deterrent to building for the NPU. Dell Pro AI Studio, a developer and IT toolkit rather than a preinstalled app, offers open, permissively licensed language, speech and vision models tested against Dell systems across silicon vendors, published on the Dell Enterprise Hub on Hugging Face.
• Enterprise readiness and on-device middleware are what local-chat demos lack. Gupta says enterprises need attestable models, control of data in and out, and lifecycle management of permissions, updates, deprecations and telemetry, which a Dell management portal provides. On-device middleware detects the silicon, pulls the intended model from a registry, loads, unloads and verifies it, replacing hundreds of lines of code developers would otherwise write and rebuild with each model change.

2025-04-13 · open the original ↗

• This post includes paid content that is not available publicly. See the original source.

2025-04-12 · open the original ↗

• Build anywhere, but integrate and test AI PC apps on Windows. Gupta argues that because NPU inference apps will mostly be consumed by Windows users, developers can do much of their work in an IDE of choice on Unix-based machines, but must integrate the model into the app, set up the local host and verify it runs on a Windows device, since that is where the app will run.

2025-04-11 · open the original ↗

• AGI needs planning over an unlimited horizon, which LLMs lack. Burkov, in an episode 867 clip, contrasts crows and monkeys that plan a day or two ahead with humans who plan for generations, and says LLMs are reactive: an "agent" has only the agency its system prompt gives it. He doesn't hazard a timeline, likening AGI to teleportation. The host suggests it may need brain-like modules such as a prefrontal cortex rather than a scaled-up transformer.
• Models can't tell what they don't know, and fine-tuning hacks don't fix it. Burkov says o3-mini's visible reasoning admitted it knew nothing of his new book, released after its cutoff, yet its answer praised the book as if fact. He says Qwen models fine-tuned to say "I don't know this person" denied knowing him, then named him as author of his first book.
• Doubling down on embodied experience is the human edge over AI. In an episode 873 clip, a digital-twin entrepreneur named Natalie argues AI is part of our cognitive evolution as language is, but not all of us. Embodied, serendipitous experience, like the host's childhood browsing of dictionaries, builds insight AI can't replicate, so people should collaborate with AI and use it to free themselves to be more human.
• The AI stack looks different to executives and developers. Richmond, a MongoDB software engineer, says in an episode 871 clip that executives see three layers: application (such as Cursor), tooling (such as MongoDB) and compute (such as Nvidia). Developers need more, starting with programming language, since many libraries exist only in Python or JavaScript, then model provider and database.
• Google's tuning playbook tries to make neural-network training systematic. Varun, a former Google engineer and first author, says in an episode 869 clip that the playbook moves training from "alchemy" toward chemistry. Architecture-agnostic, it helps practitioners choose the next experiment given a compute budget. It was released as a Markdown file on GitHub so others could fork it, and had 28,000 stars at recording, per the host.

2025-04-10 · open the original ↗

• Missing data, not models, is the Global South's AI barrier. Kallott, founder and CEO of Amini in Nairobi, says 2.6 billion people are unconnected and so "invisible" to training data, and Africa has more than 2,500 languages, many only spoken. The continent has only 1/8 of the minimum density of meteorological stations. Her team set out to build an AI company for Africa, found there was no data, and pivoted to data infrastructure, starting with environmental data.
• Global earth-science models lose accuracy on African ground. Kallott contrasts large US monocrop farms with African farms of around one square kilometre growing several crops, so open satellite imagery at 10 metres has to be super-resolved toward one metre. Readings are calibrated against ground truth, such as soil samples and yields collected by data partners. Amini worked bottom-up, starting with one region of Kenya, because she says East Africa has little cloud cover and the West a lot.
• Amini built its own geoprocessing engine to beat cloud cover. Over about a year and a half it built a Google Earth Engine–style system that layers multispectral, hyperspectral and synthetic aperture radar satellites and applies cloud masking. It also takes in drone imagery, tabular data and IoT sensors, including weather stations built into a startup's e-bike chargers.
• Revenue comes from insights; models and community access stay free. Kallott says customers are global exporters of agricultural and mineral commodities wanting first-mile data, and governments mapping their resources, while developers never pay. Version 0 of the foundation model went on Hugging Face the month before, with the next version in training and a first ICLR poster accepted. Amini runs hackathons on Zindi, such as telling cocoa from coconut trees in Côte d'Ivoire. Expansion to the Caribbean and Southeast Asia rests on her finding that about 70% of work by agro-ecological zone carried over.
• Africa must become "AI-ready" before it can leapfrog. Kallott claims Amini is the only company doing this and wants to build "an ecosystem, not a product". She compares it to Palantir digitising government data more than 10 years ago, though not in defence. She argues the work is best done by teams "born from within", who build with a UPS and generator through power cuts and train foundation models on office workstations.

2025-04-10 · open the original ↗

• Local accelerators give low latency for persistent real-time work. Gupta's own mnemonic starts with "accelerated": an on-device NPU suits latency-sensitive, always-on tasks such as translation, transcription and captioning.
• On-device AI can learn an individual user's style. For "individualized", Gupta cites a healthcare customer auto-generating ER physicians' reports from their diagnoses; a physician reported that as the model saw his edits it began to sound like him, freeing time for patients.
• Data never has to leave the device. For "private", Gupta argues local models avoid sending confidential or PII-laden data even to a private cloud tenant.
• Offloading inference to PCs cuts data-centre and per-token costs. For "cost", Gupta says a financial-services customer found 15% of its data-centre compute going to developers' code generation and completion, work he believes NPUs can take over without H100s, and cloud inference bills per token. He concedes cloud inference costs are falling, but argues for "the right engine for the right workload". Edge vision cases include real-time factory defect detection and an insurer classifying damage photos into rough cost sizes to get adjusters "maybe 70% of the way there".
• Dell works with chip partners so models run across NPU silicon. Asked about chips from Intel, Qualcomm and AMD, Gupta says the NPU architecture is so new that Dell is preparing models with its silicon partners. This lets developers target different chips without starting over, one reason Dell built Dell Pro AI Studio.

2025-04-09 · open the original ↗

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2025-04-09 · open the original ↗

• Marketing science exploits predictable irrationality using experiments and models. The guest, an Amazon marketing-science leader formerly at Walmart's Sam's Club and Nielsen, opens with Dan Ariely's Economist offer: $59 online, $125 print, $125 print plus online. With all three options, 84% of students picked the combo and 16% online-only; without the decoy print option, the split became 68% online-only and 32% combo. He lays out five pillars: experimentation, attribution, machine learning, brand measurement, and optimization and automation.
• Target persuadables, not the people propensity models favor. He sorts customers into lost causes, sure things, do-not-disturb and persuadables, and says propensity models waste discounts on people who would have bought anyway. Uplift modeling, fed by experiments, is what he credits for over 100% lift at Sam's Club and a 2020 award. He cites an eBay experiment in which pausing branded search ads shifted clicks to organic results with no lost sales, though he calls some branded spend a defensive play. He also reports direct mail as one of the most incremental channels in one past role.
• Use media mix models offline and multi-touch attribution for digital. He describes media mix models as aggregate, market-level regressions on about two years of data that control for seasonality and promotions, and says they have existed for 40-plus years. Multi-touch attribution compares customer paths to credit each touchpoint, and cross-channel effects require a DAG, for example TV driving search but not the reverse. He says neither is truly causal until its recommendations are tested against business as usual in a randomized trial.
• Win stakeholders with small staged tests against their own KPIs. He offers a test in four markets of the team's choosing, then scales from 5% to 25% to 50% of the network. In one case, a model that called for cutting spend about 90% showed about 65% in its first test. He says roughly 80% of experiments fail, and he names leadership acting on anecdotes as the most common mistake.
• Cost-center marketing tolerates cuts that top-line marketing never will. In Amazon's tier-one hiring, where recruiting-marketing decisions were decentralized in 2022, his team centralized them behind a media-mix-driven product. He reports a 90% cut in marketing spend over two years with no impact on hiring, stressing that this covers only his team's scope and is his personal view. He says he has tried and failed to get revenue-driving teams to make such cuts. His forecast is that 2025–26 looks like 2023–24, and in about five years generative AI hits creative work first and leaves smaller, specialized teams.

2025-04-09 · open the original ↗

• The basis trade turns ordinary bond-market stress into a liquidity crisis. Hedge funds borrow against Treasuries at 50–100x leverage; when markets get shaky they face margin calls, forcing Treasury sales that push yields up and can freeze what is normally the world's most liquid market — the same mechanism that seized up in 2020.
• The author suspects sovereign sellers, not just hedge funds, are driving the current sell-off. China, the UK, and Japan are among the largest foreign holders of Treasuries and are simultaneously facing the largest US tariffs, which he flags as a likely (though not confirmed) motive for reduced demand — an inference, not a reported fact.
• A rising 10-year yield is the signal to watch, since Treasuries underpin rates across the whole economy — mortgages, credit cards, and beyond. The author argues this is an avoidable, man-made crisis caused by chaotic tariff implementation, and that Washington needs to restore market confidence quickly or risk permanently denting global demand for US Treasuries.

2025-04-09 · open the original ↗

• NPUs are power-efficient matrix-math engines new to ordinary PCs. Gupta says NPUs reached the market about a year before recording, first in Intel's Meteor Lake chipset, joining the CPUs and integrated or discrete GPUs PCs already had. Being purpose-built, almost hard-coded, for the matrix multiplication and addition underlying AI, they use far less power than running models on a CPU or GPU, which would drain a laptop battery.
• For now NPUs are for inference, while GPUs still build the models. Gupta expects data scientists and developers to keep using GPU workstations for training and fine-tuning. NPUs will serve the knowledge workers who consume the results, from assistants answering questions over a knowledge base to AI embedded in workflows and, he foresees, multi-agent systems, without tanking battery life.

2025-04-08 · open the original ↗

• Clearly IP's free Open Page module unlocks formerly paid paging features. It works with FreePBX and Tango PBX version 17; install by switching to the Clearly IP repository, then running fwconsole ma downloadinstall openpage over SSH, which adds an advanced paging section to page groups.
• Pages can carry a lead-in tone and go out by multicast. A prepend announcement plays a system recording, such as a chime, before the live page; one or more multicast addresses let large numbers of phones be paged without consuming phone-system resources.
• Valet and scheduled paging suit noisy sites and schools. Valet paging records the whole message first and can queue several pages to play in turn; scheduled paging runs pages on a recurring timetable like a school bell schedule, with three bell sounds included or your own uploaded.

2025-04-08 · open the original ↗

• Backlogs cost work without delivering customer value. Henriksen, who runs the technical side of a legal-tech startup, made the case in a Copenhagen DevFest talk pairing "ditch your backlog" with Shape Up, which his startup uses. He says it drew workshop requests from companies. Raw user stories are "placeholders for communication" whose context has faded or gone by the time anyone discusses them, and grooming and reprioritizing them is effort taken from the customer.
• Backlogs also trap teams in cognitive biases. He names recency bias (newer items get more attention), the IKEA effect (people overvalue items they put work into) and the sunk-cost fallacy (we specced it, so we must build it). His answer is that nothing is more wasteful than building the wrong thing, which also costs the chance to build the right one.
• Every choice should have a stated reason, including keeping the status quo. Henriksen, who has taught software engineering at a local university for about 12 years, points to the repository pattern (which he dislikes) and Scrum as things adopted on a book's authority. Appeal to authority is fine for a beginner, he says, but not with 5–10 years of experience. His team started with MediatR, then weighed Wolverine and the "Critter Stack" and Steve Smith's endpoints library, and kept MediatR for reasons it stated. Keeping something because there is no time to change is also deliberate, he says, as long as you see that you are making a decision.
• He cites Microsoft's Go-based TypeScript compiler as a deliberate choice. He reports that the rewrite promises up to 10 times the speed. By his account, Anders Hejlsberg explained that the team evaluated many languages and chose Go for native code and a style close enough to TypeScript's for a near one-to-one port, where C# would have meant a new paradigm. Henriksen adds that the developers were TypeScript people, so they had to learn something either way.
• Unexamined stacks lead into the "COBOL trap". He gives COBOL banks whose developers are retiring, AngularJS, .NET Framework and a system built on Delphi as examples of postponing a problem that hits hard when it comes. Staying put can be fine if the reasons are named. Each reason is smaller than the whole problem and can be removed one at a time toward a destination. He applies the same deliberateness to jobs, promotions and friendships.

2025-04-08 · open the original ↗

• NPUs are inference engines for end users, not training hardware. Gupta, of Dell, says NPUs arrived about a year before recording with Intel's Meteor Lake and are near-hard-coded for matrix math, so they run AI far more power-efficiently than a PC's CPU or GPU. GPUs remain for training and fine-tuning. He puts acceptable output at 15 to 20 tokens per second, which limits today's NPUs to quantised models of about 8 or 9 billion parameters, now capable enough to make local inference practical.
• His "AIPC" mnemonic makes the case for on-device AI. Gupta's letters stand for accelerated (low latency for translation or captioning), individualized (an ER report generator that learned a physician's style), private (data never leaves the device) and cost. On cost, a financial-services customer found 15% of its data-centre compute going to developers' code completion, which he thinks NPUs could absorb. He concedes cloud inference is getting cheaper and that not every use case belongs on the device.
• Dell says its toolkit cuts NPU app delivery from six months to six weeks. Gupta describes Dell Pro AI Studio, in early access at recording and due mid-2025, as three parts. Validated models on the Dell Enterprise Hub on Hugging Face end trial-and-error pairing of models and machines; a management portal adds attestation, permissions, updates and telemetry; on-device middleware detects the silicon and loads the right model. By Dell's estimate, a typical app such as a RAG chatbot goes from discovery to fleet deployment about 75% faster.
• Build on workstations, scale on servers, deploy to AI PCs. Gupta advises embedding data scientists with the people whose problem needs solving, then moving production inference to AI PCs if H100-class hardware isn't needed. His example is a healthcare customer that trained a custom vision transformer on its own radiology images and reports physician-level diagnostic accuracy. It now runs in a HIPAA-compliant data centre, and the customer wants to bring it to AI PCs for doctors worldwide.
• On-device agents await small reasoning models; hybrid compute comes sooner. Gupta forecasts agents acting on users' behalf within set parameters, such as buying concert tickets on approval, but says reasoning models must first shrink enough to run locally, which "might take some time". He also expects workloads to shift seamlessly between device and cloud as compute needs demand.

2025-04-07 · open the original ↗

• This post includes paid content that is not available publicly. See the original source.

2025-04-07 · open the original ↗

• The 2025 tariffs were calculated off trade deficits rather than reciprocal tariff rates, and a trade deficit alone isn't inherently a problem — it can simply reflect other countries producing goods more cheaply. The author argues this miscalibration, layered onto chaotic rollout, injected volatility unseen since the March 2020 pandemic onset despite nothing being systemically wrong with the financial system.
• Retaliatory tariffs will raise consumer prices regardless of stock market exposure, since import costs are typically passed through to buyers, meaning higher prices, fewer choices, and lower average quality — effects that reach beyond the over 100 million Americans invested in stocks.
• Trump-era tariffs have a precedent of requiring taxpayer-funded bailouts: the 2018–2019 tariffs during Trump's first term damaged American farmers badly enough that the administration subsidized them with $28 billion in taxpayer money, which the author cites as evidence the current cycle risks repeating that cost.

2025-04-07 · open the original ↗

• Merck's display dominance plateaued, so it moved into semiconductors. Beckmann, who heads Merck KGaA's 8,000-employee electronics business, says its late-1960s and 1970s liquid-crystal research took decades to commercialise. At peak, roughly 2005 to 2010, it held 80% of the liquid-crystal materials market. Recognising that every innovation ramps then plateaus, it entered semiconductors around 2014 and built a leading portfolio through acquisitions including AZ, Sigma-Aldrich's high-tech business, Versum and Intermolecular.
• Incumbents' worst innovation mistake is assuming yesterday's success lasts. Beckmann argues that innovating inside an established company is far harder than in a startup because "you are in your own way", and calls believing that what worked yesterday will work tomorrow probably the most fatal mistake in business. He traces Merck from its 1668 founding after the Thirty Years' War to what he says was industry's first quality promise to customers around 1850.

2025-04-06 · open the original ↗

• High-bandwidth memory depends on hundreds of specialised materials. Beckmann says HBM needs the best possible DRAM performance, and DRAM capacitors rely on high-k materials where he says Merck leads. Spin-on dielectrics are among many promising materials, but with hundreds or thousands of precursors drawn from 80% of the periodic table, he declines to single out one as most important.
• Light-based data transfer promises speed and energy gains. Beckmann says photons move data faster than electrons and without the same heat, and that Merck reorganised its electronics sector the year before recording to create an optronics unit built on its display-era expertise in generating and manipulating light.

2025-04-06 · open the original ↗

• US tariffs are unintentionally hurting Russia's economy. He argues tariffs create economic uncertainty that cuts demand for oil and natural gas, and says 63% of Russia's exports are fuel and energy products. The captions cut off mid-sentence after that figure.