Souk's Notes - Unofficial Summaries

2025-03-01 · open the original ↗

• Without an upfront value hypothesis, AI projects fail on ROI. Al-Dhubaib says data scientists get enamored with model accuracy instead of asking which decisions the model influences, what a successful intervention is worth, and how often the decision will actually change.
• A risk score is useless unless it tells providers what to do. In a hospital project on solid-tumor cancer patients, whose national readmission rate he puts at about 25%, clinicians responded to risk alerts with "now what?". The team added interpretability showing why a patient was at risk, such as medication, comorbidity or a zip code with unreliable transport, pointing to interventions like social assistance.
• Stress-test the value estimate with the client before investing. In the readmission example the team assumed it could identify 80% of at-risk patients but only 20% would receive an intervention, valued the project on that basis, and asked whether it was still worth the investment, revisiting the hypothesis as models were validated.

2025-02-28 · open the original ↗

• Warp breaks terminal conventions to behave like a notebook. Lloyd says the mouse and text selection work as in a normal editor, and each command is married to its output as a "block" that can be shared, filtered or attached as AI context. He accepts some compatibility cost for a native GUI over "terminal purity": "rules are made up."
• Agent mode drives the terminal, asking approval by default. An on-device classifier decides whether input is a command or English; agent mode can run commands such as git status to gather its own context, as in his merge-conflict example. Users can opt into LLM-judged read-only commands or allow and deny lists; models come from OpenAI, Anthropic and DeepSeek (R1), with Claude 3.5 Sonnet the default "as of today".
• AI, not collaboration, is what now pays for a terminal. Asked about a reported $50 million raise, Lloyd says free usage made the early years stressful, and Copilot and Cursor changed developers' expectation to paying for AI's marginal cost; he says revenue is "growing pretty well". His stated aim is "the world's best place where developers can build software", with the terminal as the starting point, and Warp Drive extends shared team commands into context for its AI.
• Windows came last because Warp went deep on Mac first. Founded June 2020 and written in Rust, Warp shipped on Mac, then Linux; Windows was the top feature request, serious work began about a year before recording, and the Windows build launched the day before. Lloyd says he would now do Windows before Linux and concedes Windows Terminal is more polished in places; the episode is sponsored by Warp, and Hanselman says he spent hours with the team on its Windows experience.
• Offline-first, with AI and telemetry under user control. Everything except AI and real-time collaboration works offline, one toggle removes all AI, and local models via Ollama are being looked at but not built. Smart completions with flag explanations come from a local database built on open-source completion specs.

2025-02-28 · open the original ↗

• Even a right-leaning veteran calls Trump's Ukraine moves damaging. Rebel Intel, a former military intelligence analyst who voted Republican, says the US voting with North Korea and Russia at the UN set the wrong tone, and that Trump's claims that Ukraine started the war and Zelensky polls at 4% are "mistakes of the heart" that diminish US standing. He wanted Trump to show strength to Putin instead.
• Elites engineer or exploit chaos to consolidate power. Rebel Intel's master's paper, "order out of chaos", outlines about 30 scenarios and cites Mother Jones that the US hosts 25% of the world's billionaires and 35% of its multimillionaires. The host agrees in part: tax cuts and emergency money-printing let asset holders outrun inflation while the middle class falls behind.
• Ukrainians, not the US, drove the Maidan, the guest concedes. Pressed by the host, Rebel Intel says US influence operations existed, as they do everywhere, but agrees Russia used the uprising as a pretext for Crimea. He still believes the 2022 war was in some sense engineered, while both agree lethal aid should have flowed from 2014.
• His Trump vote was for Republican domestic policy, not the man. Rebel Intel says he agrees with much of Project 2025, which he estimates Trump is over 70% aligned with, though not its veterans-benefit cuts. He defends Hegseth as someone with room to grow and Gabbard as an anti-interventionist who warned Assad's fall would empower jihadists; the host rejects both and cites Trump's personal conduct.
• Defense budgets will rise and political violence may follow. Rebel Intel expects about $50 billion more, taking the budget past $900 billion, noting it fell only in 1993–1997. He sees fertile soil for Spain-1935-style street violence by "small kill teams" rather than civil war, and says Trump needs a felt economic improvement within a couple of years.

2025-02-28 · open the original ↗

• AI services are Cognitive Services under a new name. Giard opens the project created in the previous episode at ai.azure.com and finds AI services in its left-hand menu, in four categories: speech, language and translator, vision and document, and content safety. Vision and document has three tabs: document, face and image. Every service he shows returns JSON over REST calls, and most have an SDK, sample code in Python, C# or JavaScript, documentation and a quick start.
• Document templates parse receipts into structured fields. The document tab has templates for invoices, receipts, ID documents and credit cards, plus a generic optical character recognition "read". Run on a sample receipt, it found the address and split it into house number, road and postal code, and listed the line items. It also parsed a hotel receipt of Giard's own.
• Face detection returns mask status and facial landmarks as JSON. For each face it reports whether a mask is worn and whether it covers the nose and mouth, the face rectangle, and landmark positions such as the pupils, nose tip, mouth ends and eyebrows. A photo with three faces came back as an array of three. Giard notes that the bounding box and landmark dots are drawn by the sample app's JavaScript; the service itself returns only the JSON.
• Object detection and captioning label image contents with confidence scores. Object detection returns each object with a bounding box and a confidence, e.g. 98% for a skateboard and 95.5% for a person in a sample, and 57% for a bird in Giard's own photo of a guitarist. Image captioning gave one caption for his bird photo, "a green bird on a branch", which he calls "pretty darn accurate". Dense captioning adds captions for different regions of the same photo.
• OCR returns text by line and word, each with its bounding box. On an image of a poem it split the text into lines, and each line into words, with the X, Y coordinates of the four corners of each box. Common tag extraction returns a list of tags with relevance scores, e.g. 96% for a picture frame and 95.5% for a gallery in a photo of a painter in an art gallery.

2025-02-28 · open the original ↗

• Investors are pricing de-extinction on its side technologies, not revenue. The host reports that Colossal Biosciences raised a $200 million Series C at a valuation over $10 billion, nearly sevenfold its valuation a couple of years earlier, with no revenue yet and its main projects not projected to complete until 2028.
• The method is genome comparison, CRISPR editing and a surrogate. As the host describes it, Colossal maps the extinct species' genome, compares it with a close living relative (the Asian elephant for the mammoth), edits the relative's cells with CRISPR to match, and implants the resulting embryo in a surrogate; it has also built artificial-womb technology the host says could apply to human fertility treatment.
• Colossal makes money three ways while chasing its core mission. The host lists spin-offs (one using microorganisms to break plastic waste into organic matter, and Form Bio, a computational biology platform for gene therapies, both with millions in further funding), conservation work with governments on species such as the northern white rhinoceros, of which two females remain, and an emerging biodiversity-credits market modelled on carbon credits.
• De-extinction needs good DNA, a living surrogate and a surviving habitat. On those conditions the host rules out dinosaurs; he names the dodo and Tasmanian tiger as active Colossal projects, the passenger pigeon as a Revive & Restore project, and the Caribbean monk seal, quagga, great auk and heath hen as other candidates.
• The host's lesson is that impossible goals throw off valuable byproducts. His opinion is that each of Colossal's indirect applications could eventually generate billions of dollars in revenue on its own, and he urges listeners to use AI tools such as OpenAI's and Google's Deep Research features, new "as of the past few weeks", to dream up ambitious goals.

2025-02-27 · open the original ↗

• A chat model is a "zip file" with a smiley face drawn on it. Pretraining compresses a lossy, probabilistic gist of the internet into roughly a trillion parameters, which is why it has a knowledge cutoff (GPT-4o's is many months to a year old) and hazier recall of rare topics; post-training then swaps in human-written conversations to give that same network the persona and style of a helpful assistant, without adding new knowledge.
• Reasoning models trained with reinforcement learning solve problems plain models can't, by practicing until they find their own inner monologue. Given a buggy gradient-check, non-thinking GPT-4o suggested only surface fixes, but o1 pro thought for about a minute and correctly found a parameter-packing mismatch; Claude 3.5 Sonnet, Grok 3, and DeepSeek R1 (via Perplexity, showing raw chain-of-thought) all solved it too, some without dedicated "thinking" mode.
• Tool use turns the model from a guesser into a verifiable analyst, but only if you check its work. Asked to plot and extrapolate OpenAI's valuation, ChatGPT's Python code silently assumed $100M for a missing 2015 figure and then reported a $1.7 trillion 2030 estimate that contradicted the $20 trillion its own printed variable actually held — a "junior analyst" mistake caught only by rereading the code and reprinting the number.
• Any modality can be tokenized the same way text is, which is what lets one model natively read, hear, and see. Audio is chopped into spectrogram chunks and images into patches, each quantized against a vocabulary exactly like word tokens, so an "Omni" model processes speech or pictures directly — unlike bolted-on pipelines that just transcribe audio to text and back, which he calls "fake audio."

2025-02-27 · open the original ↗

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

2025-02-27 · open the original ↗

• Repeat clients, not interest, revealed Pandata's product-market fit. Al-Dhubaib says that because he bootstrapped, paying customers were his only source of growth; when he asked one of the few returning clients why it came back, the answers were his firm's plain, jargon-free approach and an upfront scoping phase it had stumbled into.
• A mandatory paid discovery phase screens out the wrong clients. He made a 30-day to six-week "discovery and design" engagement compulsory, priced at $12,000 at the time and $50,000 now, to find "where the skeletons are" and the unknown unknowns; it filtered out clients who weren't serious and those who didn't accept the data science process and would end up disappointed.
• The filter nearly broke the firm before it paid off. Al-Dhubaib says his pipeline dried up and the bank account fell below a month of expenses, one of three such times, but in that same period he landed three of his biggest clients, two of which stayed for about six years until Pandata's exit.
• Saying no to small, low-value work is what lets a consultancy scale. He calls ruling clients out a counterintuitive lesson that frees time for higher-value work, and says the acquirer's due diligence flagged about three points in the financials that matched the moments he had regretted too.

2025-02-27 · open the original ↗

• Conservative creators' worry about looking bad comes years late. He responds to a Republican creator telling peers to stop claiming the no-tax-on-tips and overtime bill had passed, saying that ship sailed about 8 years ago. He cites the belief that kids were getting sex changes in school, and the idea that renaming the Gulf of America let drilling restart, when he says drilling never stopped.

2025-02-26 · open the original ↗

• A hub is the top-level resource; projects are containers inside it. Giard describes Azure AI Foundry as a unified platform to explore, build, test and deploy AI applications. The hub is its primary top-level Azure resource, a project is a container for your AI work, and one hub can hold several projects.
• Creating a project also creates or selects its hub. At ai.azure.com, the Create Project button asks for a project name and a hub, either an existing one or a new one. A new hub name must be unique and between 3 and 33 characters.
• The hub step provisions the supporting Azure resources. Giard sets the subscription and creates a new resource group, keeps East US as the location, creates a new Azure AI service or OpenAI service, and also creates an Azure AI Search resource, which is optional. Validation checks for problems such as a region where the services are not available, and the Create button is enabled only when there are none.
• Provisioning took about three minutes in the demo. Status icons show each resource, including the project, a storage account and the AI Search account, as queued, in progress or created. The finished project shows an API key and a left-hand menu with model catalog, playgrounds and AI services. Giard notes that a few of the items are in preview.

2025-02-26 · open the original ↗

• The failed startup's free research was the real product. Al-Dhubaib says his first venture, built out of computational neuroscience work at Case Western, raised money, entered an accelerator and collapsed in about a year and a half; hospital researchers he had been doing free pilot research for then told him they had depended on it, and would pay for it.
• Pandata was founded on a regional data science talent gap. At its start, at the end of 2015 and start of 2016, he says Northeast Ohio had fewer than 150 data scientists, about a third of them at IBM, the Cleveland Clinic or Progressive Insurance, while other large local enterprises had one or two.
• The firm survived by moving to whatever was still hard. He says it shifted from analytics and business intelligence to machine learning, then to machine learning in heavily regulated environments as each layer became commoditized; after about eight bootstrapped years it was acquired by Further, where he is now head of AI and data science.
• "Make it boring" became a core value because clients who understand stay. Al-Dhubaib says many data scientists of the time liked being opaque, while he found clients who grasped the work hired the firm and stayed longer, so he explains complex mathematics with cats, dogs and memes.

2025-02-25 · open the original ↗

• The system has no enforcer behind it; it runs on consent. The guest, Brian, a former Assistant Attorney General in American Samoa, says nobody knows what happens if a president ignores the Supreme Court. He counts only two clear defiances — Andrew Jackson, and Lincoln's wartime suspension of habeas corpus — with FDR threatening but never needing to, and says both times the country simply let it go.
• McConnell's blockade of Garland was the first real break, the guest argues. He calls refusing hearings for Merrick Garland a fundamental violation, since justices are meant to arrive at random times rather than through engineered ideological purity. He adds that most Supreme Court cases are unanimous — recalling a figure "in the 80s, 90s" percent — so the system is less dire than headlines suggest.
• Executive orders reach far less than Trump and Musk assume. In the guest's view they bind only executive-branch employees, not the private sector, and not most agencies, which Congress created. He sees no constitutional damage yet, but concedes appointees could harm military and intelligence capability; the danger point is Congress acquiescing or the president defying court orders.
• Distrust grew from real grievances exploited by people who knew better. The host, arguing devil's advocate, traces working-class alienation to the Great Recession bank bailouts and cites Trump's corporate tax cut from 35% to 21% as driving deficits. The guest says Tea Party-era lawyers he watched in law school around 2010 knowingly misrepresented basic law, which prompted his civics videos.
• Gingrich's four-day congressional week broke bipartisan social life. The guest says that around 1994 the change let members fly home to campaign and leave families there, ending shared Northern Virginia neighborhoods; younger Republicans bunked together in places like the C Street house. Bartending in DC from 2002–2003, he heard locals still describing how the town's tone changed.

2025-02-25 · open the original ↗

• The UCG-Fiber lets any copper port serve as either WAN. It has four 2.5 Gbps RJ45 ports, one 10 Gbps RJ45 port and two 10 Gbps SFP+ cages, and any of the 2.5 or 10 Gbps ports can be set as primary or secondary WAN. It runs UniFi OS with the full app suite (Network, Protect, Access, Talk), and an M.2 NVMe slot provides Protect storage.
• The host got full LAN wire speed in testing. He set WAN on a 2.5 Gbps port and ran a 10 Gbps speed test across the LAN. The figure of just over 5 Gbps WAN-to-LAN with IDS/IPS enabled is stated, not shown tested. Base price is $279 with no storage; the drive tray is a separate accessory, and 1 TB and 2 TB NVMe versions are also offered.

2025-02-25 · open the original ↗

• The UDR 7 is an all-in-one gateway needing no separate controller. It runs UniFi OS and UniFi Network on board, so no Cloud Key or separate UniFi Network controller is needed.
• The UDR 7 adds an SFP+ port and dual WAN. Around the back are four 2.5 Gbps RJ45 ports, one with PoE output for an access point, VoIP phone or camera, plus a 10 Gbps SFP+ slot; internet can run off RJ45 or SFP+, and dual WAN is supported.

2025-02-25 · open the original ↗

• The UniFi Express 7 upgrades the original Express to Wi-Fi 7. The UX7 is physically larger than its Wi-Fi 6 predecessor and has a 2.5 Gbps RJ45 LAN port, a 10 Gbps RJ45 WAN port and 2x2 Wi-Fi 7. In the box are a USB power brick with cord and a braided patch cable.
• Like its predecessor, the UX7 runs UniFi Network only. The host found Network was the only application offered, so it cannot run Protect, Access or Talk. Setup was as simple as other UniFi gateways, and he updated it to UniFi Network 9.0.1 without issue. As of recording he did not know the MSRP or how many UniFi devices it can manage.

2025-02-25 · open the original ↗

• The UCG-Fiber is the host's pick for multi-gig internet connections. It has a quad-core ARM Cortex-A73 CPU and 3 GB of DDR4 RAM, and Ubiquiti claims over 5 Gbps of throughput with IDS/IPS enabled. The host did not run a multi-gig WAN-to-LAN test himself and asked viewers to post theirs.
• Storage costs extra on the base model. The $279 base model has no NVMe drive; adding your own requires the $19 M.2 drive tray. Versions with 1 TB and 2 TB pre-installed were launching, but the host had no pricing on them at recording. Like the UCG-Max, the NVMe slot is for Protect surveillance storage.
• Any of the seven ports can be primary or secondary WAN. There are four 2.5 Gbps RJ45 ports (one with PoE+), a 10 Gbps RJ45 WAN port and two 10 Gbps SFP+ cages, one WAN and one LAN by default. The host put WAN on a 2.5 Gbps port and disabled secondary WAN, freeing all three 10 Gbps ports for LAN.
• The host measured full LAN wire speed in his lab. He got 10 Gbps throughput running OpenSpeedTest on a Minisforum MS-01 over a DAC on an SFP+ port and testing from a Mac Mini on the 10 Gbps RJ45 port. He suggests 2.5 Gbps WAN users can use the three 10 Gbps ports for a core-switch uplink, a NAS and a workstation.
• He expects price and mounting to be the only complaints. He struggled to find valid criticisms. It is desktop-only, powered by a separate 54 V DC brick, with a template for the rubber feet. He discloses that his purchase link is an affiliate link that pays him a commission without changing the buyer's price.

2025-02-25 · open the original ↗

• The UDR 7 keeps the old form factor with faster ports. The old UDR had a single 1 Gbps RJ45 WAN; the UDR 7 has four 2.5 Gbps RJ45 ports (one with PoE out) and a 10 Gbps SFP+ slot, adds a Wi-Fi 7 access point on top, and runs UniFi OS and Network on board with no separate controller.
• Either WAN can be assigned to any of three ports. The choices are ports 3 and 4 (2.5 Gbps RJ45) and port 5 (the SFP+). The host ran Comcast Business cable on port 4 and T-Mobile Home Internet on port 3 as dual WAN, and reassigned the SFP+ (secondary WAN by default) to LAN for a 10 Gbps DAC link to an OpenSpeedTest server.
• Wired tests reached full wire speed on every client. A 2.5 Gbps-connected Minisforum server hit full 2.5 Gbps across the UDR 7 to the 10 Gbps server, and a 1 Gbps laptop got essentially full gigabit. The setup speed test on his sub-gigabit cable line showed 932 Mbps down and 237 Mbps up.
• Wi-Fi 7 tests needed channel widths maxed out first. After forgetting this initially, he set 240 MHz on 5 GHz and 320 MHz on 6 GHz. A 2x2 Wi-Fi 6 iPhone 13 Pro Max reached roughly 770–790 Mbps, and a 2x2 Wi-Fi 7 Google Pixel 8 roughly 1.7–1.9 Gbps, though the captions garble the exact figures.
• He recommends it as an all-in-one for newcomers and small sites. He sees it suiting parents' homes or small businesses he administers, and it can still expand with access points, Protect or UniFi Talk phones. Its microSD slot handles Protect for one or two cameras; beyond that he points to a UNVR. Price was unknown at recording.

2025-02-25 · open the original ↗

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

2025-02-25 · open the original ↗

• A mandatory paid discovery phase built Pandata's durable client base. Al-Dhubaib made a 30-day to six-week "discovery and design" engagement compulsory, at $12,000 then and 50,000 now, to weed out unserious clients and those who would not accept the data science process; the pipeline dried up and cash fell below a month of expenses, then he landed three of his biggest clients, two of whom stayed about six years until the exit.
• Mid-sized regulated work too small for the big firms was the niche. He says Pandata served hospitals, life sciences, higher education, energy and utilities and some defense work, built insurance and security processes "way more secure than any of our clients", charged a premium, occasionally took projects from McKinsey or Accenture, and that this expertise was a big reason Further acquired it.
• Building users' trust lifted a failed model without changing it. In his telling, an ensemble model for a health-tech client with about 50 million patient lives found 30% more people eligible for government assistance, but its pilot converted 2 in 100 mailings against a baseline of about 10, because staff with bonuses tied to selection distrusted its unfamiliar picks; after training sessions and per-prediction explainability, the same model grew reach by 18%.
• Projects need a value hypothesis about decisions, not model accuracy. For cancer readmissions, with a national rate of about 25%, Al-Dhubaib says providers asked "now what?" until the model explained the drivers of risk, such as poor transport access; he now stress-tests value up front, for example identifying 80% of at-risk patients with a realistic 20% intervention rate.
• An acquisition works when the buyer's values and clients fit. He says he asked Further's CEO about core values within minutes of being offered a deal, to judge whether clients and staff would carry over, and diligence closed in about three months between January and March of the previous year; his hiring values, he says, are approachability, winning together, cultivating trust, pursuing growth and taming uncertainty.

2025-02-24 · open the original ↗

• Moderation ground truth still starts with human labels. The guest, a staff data scientist at Cash App who worked on trust and safety at Nextdoor, says machine learning's job is to scale the judgments of about 100 people across hundreds of millions of items; Nextdoor moved from user reports (limited per user, under-reported and weaponized against rivals) to a reviewer panel, and was starting to add an LLM as an extra panel vote, or as a replacement for a reviewer once its vote proved reliable.
• Outside illegal content, "bad" is a company's choice. The guest says most platforms optimise for clicks and remove only what is clearly illegal, while Nextdoor took a stance against incivility, which is so subjective that its panel agreed far less often than expected (exact figures withheld). The guest's remedy is to enforce what reviewers agree on and send the rest back to be rewritten as more concrete policy, while ground truth keeps shifting as big-tech policies change.
• Hiding inflammatory comments showed no measurable effect on activity. In the guest's Nextdoor research paper, comment filtering ("gray boxing") was tested with a regression discontinuity design and ANOVA around each rollout phase and produced a null result; the guest's hypothesis is that exposure is nonlinear, so going from twice a week to once matters little but once to never matters a lot.
• Data science's biggest trust-and-safety impact is winning funding. Because trust and safety is always "in the red", the guest says showing that bad content drives users to leave moved the Nextdoor team from underfunded to a company-level OKR. The guest names presenting a plain answer and recommendation, not the modelling behind it, as the most underrated skill.
• LLMs let one voice become thousands of convincing identities. The guest says people believe what many voices repeat, so AI companies need policy-driven safety upstream that filters about 99% of abuse plus a reactive team to patch the remaining 1%; the guest calls it "very optimistic" to expect them always to prioritise that, though thinks they should.

2025-02-24 · open the original ↗

• The Canada trade deficit tracks oil prices, not the USMCA. Responding to a creator, "the Older Millennial", who blames USMCA for the deficit's rise under Biden, he argues the deficit rose throughout Trump's term, dropped only because of COVID, then spiked as oil prices tripled under the 2020 OPEC deal Trump brokered in April 2020 with MBS and Putin.
• SPR releases curbed the deficit; Keystone XL would have widened it. He says the deficit would have been much higher without releases from the Strategic Petroleum Reserve, and that Keystone XL existed to raise the price of Canadian oil, so opening it would have widened the gap further.
• Billionaires profit from crashes because they buy cheap. He closes by arguing billionaires don't care if ordinary people lose everything, and will burn the market down when it doesn't deliver bargains fast enough.

2025-02-24 · open the original ↗

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

2025-02-24 · open the original ↗

• A university group could not build AutoML tools for the masses. Hutter says that after 10 to 12 years co-leading the AutoML community, whose best-known tool, Auto-sklearn, wraps scikit-learn to choose methods, preprocessing and hyperparameters, a lab with a few research engineers only ever built tools fit for researchers; reaching a broad user base, especially with TabPFN, needs a commercial entity with strong engineers.
• Prior Labs builds products while the university keeps the moonshots. Hutter, co-founder and CEO of Prior Labs, says he will keep an academic co-affiliation and research tabular-data questions there, such as how the learned algorithm works, how it shifts with the priors and what its failure modes are; PhD students gain engineers who can turn their papers into products.
• Prior Labs is hiring broadly and putting community before sales. At recording, Hutter says a funding announcement was due two days later; the company was hiring AI scientists, ML engineers, backend engineers and community staff, with sales left for later.

2025-02-23 · open the original ↗

• The story is the main idea; details are not the story. Josh Starmer of StatQuest (1.3 million subscribers, per the narrator) boils each topic to a nugget anyone can follow: PCA is "a way to give us something we can draw on a two-dimensional graph" when there are too many measurements to plot, and words like "dimension reduction" come later or not at all.
• Cap a talk at a few main ideas. Starmer stops each video at three points ("bam", "double bam", "triple bam") because beyond that audiences go numb, as at seminars that run through a thousand slides of equations; like a film, a talk should have one main thrust and at most one or two side stories.
• Use examples drawn from the audience's daily life. The goal is to communicate, not to show a deck, so he swaps statistics' abstract urns of marbles for a bag of M&M's — whether pulling only red ones is likely — a habit he says he formed explaining statistics to geneticists who avoided it.

2025-02-23 · open the original ↗

• Even TabPFN version 1 found real scientific and industrial uses. The host cites a Science paper on large-scale chemoproteomics for ligand discovery that used TabPFN in its inference, and an "awesome TabPFN" GitHub repo listing about a dozen applications across health insurance, factory fault classification, finance, wildfire propagation and biology.
• A generic method lets non-experts fine-tune into specialist domains. Hutter says he knows nothing about chemical proteomics but could fine-tune TabPFN on that field's data to beat the general model, and he names algal-bloom prediction and many medical applications; he expects finance uses exist but go unpublished.
• Prior Labs wants the open-source community to carry adoption. Hutter says he created the applications repo the day of recording and invites pull requests; within a couple of days of the Nature paper, an open-source interpretability repo had integrated TabPFN, and the host notes version 2 handles missing data, outliers and larger datasets.

2025-02-22 · open the original ↗

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

2025-02-22 · open the original ↗

• TabPFN's prior is a generator of random structural causal models. Hutter credits first author Noah Hollmann with a method that samples graphs in which features and the target are nodes with unknown causal direction; real data never reveals the true graph, so the ideal prediction is a Bayesian posterior that weights each candidate model by how well it fits.
• The network learns to approximate that posterior in one forward pass. Hutter says TabPFN never stores the roughly 130 million structural causal models used to train it; it sees only the raw data and has learned to interpolate across all of them, which the host frames as a substitute for the trillions of tokens language models get and tabular data lacks.
• Training only on synthetic data rules out test-set leakage. Hutter says reviewers rejected the first TabPFN v1 submission because its performance looked too good, suspecting tuning on the test set; the team dropped a component that made gradient updates on real datasets, lost almost nothing, and since then never touches real data in training.

2025-02-22 · open the original ↗

• Recognizing only your own commands makes local voice fast and accurate. Hansen built Rhasspy around asking "which of these things did the user say" rather than "what did the user say", using Kaldi with a bespoke language model of a few hundred kilobytes in place of a gigabyte one. He reports sub-second responses on a Raspberry Pi 4 and under a tenth of a second on a desktop, but close options such as 15 and 50 need clearer speech, combinatorics can blow up to millions of sentences, and rejecting out-of-scope speech only recently became robust.
• Local-first means choice, not rejecting the cloud. Hansen disputes Hanselman's "vote of no confidence" framing: components are modular, Nabu Casa's cloud routes speech through Microsoft Azure, and requests the local system cannot handle can fall back to ChatGPT, Claude, Gemini or a local model via Ollama. He cites quadriplegic users who ran Alexa, Google and Siri together for redundancy and wanted something more robust.
• ESPHome hardware is how voice reached Home Assistant. Hansen, who works for Nabu Casa, says its 2023 "year of voice" push extended ESPHome firmware, and the company sells the $59 Home Assistant Voice Preview Edition with an XMOS audio chip, "preview" because the software is still a work in progress. The device keeps only enough audio to detect the wake word, then streams to the user's Home Assistant server, where Whisper suits beefy hardware and an add-on he is building trains itself on the home's device names for a Pi.
• Training data, not models, limits accents and languages. He says wake-word data lacks accented English and female or high-register voices; his Kaldi models use public-domain Mozilla Common Voice data, and custom pronunciations can accommodate accents. For his Piper text-to-speech voices about a dozen volunteers record two to three hours each, Nabu Casa pays for studio time in some languages and releases the data to the public domain, and LibriVox audiobooks train poorly because narration lacks short phrases.
• Cloud assistants are declining as local LLM hardware lags. Hansen says Alexa and Google are getting worse as their makers fail to profit, while local LLM boxes cost two to five thousand dollars and are not plug-and-play with Home Assistant. He wants an in-home AI box, which would allow uses such as a vision model describing a camera snapshot every five seconds that a paid API would make too costly.

2025-02-21 · open the original ↗

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

2025-02-21 · open the original ↗

• o3-mini high is the top public model on key reasoning benchmarks. The host says that in its high mode, the one with the most inference-time compute of three (low, medium, high), OpenAI's newly released o3-mini beats o1-mini, DeepSeek R1 and the full-size o1 on the AIME math, Codeforces coding and SWE-bench Verified benchmarks.
• o3-mini matches or beats o1 at a fraction of its price. The host puts o1 at $60 per million output tokens and o3-mini at 7% of o1's price on both input and output; he says o3-mini costs about twice as much as R1 on DeepSeek's own cloud in China, but about half as much as R1 run on a US cloud provider.
• The host rates the release below R1 because o3-mini is proprietary. He says R1's open-source release gives more freedom to adapt the model and to run it on any infrastructure; o3-mini is available to free ChatGPT users through the "reason" button, to paid plans as o3-mini high, and through the API.
• OpenAI's own figures put the full o3 far ahead. The host relays OpenAI's not yet independently verified scores: on AIME, 96.7 against 87.3 for o3-mini high and 79.8 for R1; on Codeforces, an Elo over 2,700 against about 2,100 and 2,000; on SWE-bench Verified, almost 72 against about 49; on GPQA, almost 88 against 80. He expects o3's public release soon and takes OpenAI's past figures as fairly reliable.

2025-02-20 · open the original ↗

• The claim that the US lost energy independence under Biden is false. Answering a clip in which a guest says the US was energy independent under Trump and no longer is, he defines independence as producing more total energy than is consumed: a surplus of 1 quadrillion BTUs when Trump left office against 9 quadrillion BTUs at the end of 2023, which he calls nine times more independent.
• US output and exports are at record levels. He states the US produced more oil in 2023 than any country in history and expects that record to be broken again in 2024, and that it is the largest natural gas producer in history and the world's largest exporter of both natural gas and refined petroleum products.
• The outlet demanded sources from commenters but not from its guest. He criticizes the channel for broadcasting the guest's claim unchallenged while asking viewers who disputed it to cite sources, and describes his own role, including with a news organization he says he is about to join, as fighting misinformation.

2025-02-20 · open the original ↗

• The US was energy independent under Trump, and a critic conceded it. Steve Rattner said on MSNBC the US was never energy independent under Trump; after Stephanie Ruhle tagged Rattner in his rebuttal on X, Rattner said days later on Morning Joe the US had been energy independent for five years. He cites the Institute for Energy Research and the EIA showing energy independence in 2019.
• He claims a track record of getting outlets to correct themselves. He says he has been getting publications to change headlines and commentators to change on-air statements for about 3 years, work viewers do not see, and teases a new partnership to be announced within days.

2025-02-20 · open the original ↗

• The LAN Turtle gives remote shell access to any network it joins. The Hak5 device looks like a USB Ethernet adapter but is a small Linux box; the host configured it to open a key-authenticated reverse SSH tunnel to an Ubuntu server at Vultr, then SSHes to port 2222 on that server to land inside whatever network it is plugged into.
• MAC address whitelisting will not stop it. The device can mimic the MAC address of the host computer it is plugged into via USB. The host calls detecting and preventing such devices a hard question, asks viewers for their approaches, and says a follow-up video on defences is coming.

2025-02-20 · open the original ↗

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

2025-02-20 · open the original ↗

• Prior-data fitted networks are trained to compute the Bayesian posterior predictive. Hutter confirms that this is the optimization objective of every PFN, so a Transformer is carrying out Bayesian inference.
• PFNs replace hairy MCMC and variational inference with plain supervised learning. Hutter says closed-form posteriors exist only for simple cases such as Bayesian linear regression or Gaussian processes with known hyperparameters; beyond that, MCMC is slow to converge and variational inference carries approximation error, whereas a PFN samples millions of datasets from the prior and learns with cross-entropy loss to predict held-out points, approximating Bayesian inference "to an arbitrarily strong degree".
• A PFN can integrate over architectures, not just weights. Hutter says standard Bayesian neural networks place a posterior over the weights of one fixed architecture; with a PFN you can place a distribution over architectures too and get the posterior predictive in a forward pass, which he calls Bayesian neural architecture search.
• The limitation is that PFNs give no posterior over weights. Hutter says they go straight to the predictive distribution of y given x, so they cannot say which architecture or weights are right, only give the Bayesian average over all of them.

2025-02-20 · open the original ↗

• Product data science is the feedback loop for mature products. The guest, a staff data scientist at Nextdoor formerly at Amazon (Prime Video personalization, Alexa experimentation) and Twitter (trust and safety), says a 0-to-1 product needs vision rather than data, but after launch data guides every stage: opportunity sizing, discovery, roadmap, A/B testing, metric definitions and deep dives.
• Lost engagement must be argued to be bad engagement. At Twitter, the guest says, better detection of bad content dropped engagement, so data scientists had to convince product partners that this engagement was unwanted, and define the metrics and guardrails for those high-profile experiments.
• Run experiments to learn, not just to launch. The guest warns that teams who spent two to three months on a feature become launch-happy, and urges digging into neutral results for segments that cancel each other out; a co-host adds that a failed experiment yields real user data for better-informed hypotheses than throwing ideas at the wall.
• Earning partners' trust is the hardest skill to acquire. The guest expects the first couple of quarters at any new company to go to it, and does it through transparency and over-delivering by answering "the question behind the question"; the guest prefers "partners" to "stakeholders", which implies a hierarchy of ownership.
• Centralized data teams turn analysis into ticket-taking. A co-host likens them to in-house consulting without the context that embedding brings; the guest blames engineer-to-data-scientist ratios of roughly 10:1 to 20:1, while a host argues for one dedicated data person per PM. The guest names communication across engineering, PM and design jargon as the most underrated skill.

2025-02-20 · open the original ↗

• Explorer's inconsistencies are the price of decades of compatibility. Hanselman calls some of the hate toward Windows 11 File Explorer unfair but shows its rough edges: a flash of light mode in dark mode, and modern menus that open older-looking dialogs. He says Explorer carries COM objects, XAML islands and plug-ins that a from-scratch app can ignore, and that search improvements are underway.
• Files is a polished open-source alternative aimed at aesthetics. He believes files.community is largely one main developer with many contributors; it lists WSL and cloud drives such as Dropbox separately, gives removable drives an eject button, and adds a Git column showing status and the latest commit message. It also enumerates the shell extensions plugged into Explorer under "show more options", and supports tear-off tabs; it is on the Windows Store.
• File Pilot is a tiny from-scratch beta built for speed. The whole app is 2 megabytes, it is free during the beta with a license mentioned for the future, and he guesses it is written in C or possibly Rust. He shows instant Ctrl+T tab spam, a fast filter and an Everything-style global filter, asynchronous folder sizes on hover, middle-click split views, and an open-console command, but warns its default font is "shockingly small" and notes there is no cloud-drive call-out yet.
• The competition is good for Explorer. Hanselman calls the two apps "effectively a vote of no confidence against Explorer" but welcomes them as unapologetically Windows-native apps that give the Explorer team something to aspire to. He also points to Midnight Commander, by Miguel de Icaza, as a Norton Commander-style option.

2025-02-19 · open the original ↗

• Mark wrote ZoomIt for his own Windows internals demos. He says he built it because a Toshiba laptop zoom utility was clunky for the Sysinternals demos he gave with Dave Solomon, and it has evolved for 20 to 25 years. Most of it sits in one C file around a 7,000-line function; it now ships in PowerToys and is open source in the Sysinternals GitHub repo, where Mark says a refactoring PR would be considered.
• ZoomIt is an everyday tool, not just a presenting aid. Mark says he zooms almost daily on fonts he cannot resize, and Hanselman calls it the first thing he installs. Against Windows Magnifier, which they describe as an accessibility tool with a floating toolbar, they stress ZoomIt's keystroke-driven annotation, which Hanselman likens to John Madden drawing on screen; he says people with eyesight impairments and teachers use it too.
• The feature set runs from zoom-and-draw to live zoom and recording. Mark demos Ctrl+1 to freeze and zoom, drawing with shapes, letter-keyed colors, highlighting and blur, typing, Ctrl+2 to annotate unzoomed, and the newer live zoom that annotates a moving screen. Ctrl+6 copies a screenshot or crops one to file, and Ctrl+5 records the screen or a region; there are also break timers and black or white whiteboard screens.
• Fluency in ZoomIt comes from drilled practice, like a kata. Hanselman argues that pointing, talking and drawing at once only looks effortless because it is practiced, and grades it in improvised belts from simple zoom-and-click up to typing while talking. His tips are to point at the target before Ctrl+1 so the zoom lands there, and to avoid nauseating the audience, though he could not demo live drawing because his build lacked it.
• DemoType replays prepared code as if typed live. Hanselman says he pitched it to Mark after the joke site hackertyper, and Mark built it with others' contributions: snippets in a text file, split by start and end markers, are typed out automatically or driven by random keystrokes, and can include Enter and arrow keys to walk through IntelliSense. Hanselman concedes it is not yet in his muscle memory, though he says it improved his South Africa keynote.

2025-02-19 · open the original ↗

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

2025-02-19 · open the original ↗

• Deep learning hasn't helped tabular data because its features already exist. Hutter says tabular datasets are mostly small and unlike one another (omics blood values for Alzheimer's against transactions for fraud), lack the spatial regularity that convolutional networks exploit, and come with human-designed features; a data scientist's higher-level engineering, such as computing BMI from height and weight, is what adds value.
• TabPFN first beats XGBoost and random forests on their own information. He says the first step was an apples-to-apples test using only raw values and labels, not column headers; combining it with language models that read headers is in progress with "some initial results", and he expects it to be "so much better".
• TabPFN does tabular learning by in-context learning, like GPT. Hutter says the whole training set and test inputs go into the prompt and the model outputs the test labels, trained with cross-entropy, so each dataset is one training example; lacking trillions of quality real tables, the team generated all training data synthetically, which he believes makes theirs the first paper to reach state of the art on synthetic data alone.
• Sampling enough datasets from a prior makes a Transformer exactly Bayesian. He cites the team's 2022 paper "Transformers Can Do Bayesian Inference": a model fitted to many datasets drawn from a prior approximates the posterior predictive under that prior, and with an arbitrarily large Transformer and minimal loss it recovers exact results, such as true Bayesian linear regression or a Gaussian-process posterior.

2025-02-19 · open the original ↗

• "Squishy" human experiences can be measured and predict real outcomes. Hicks, who runs Pluralsight's Developer Success Lab, says psychology builds scales tested across thousands of people, likening its measurement error to atmospheric dust clouding starlight. Sense of belonging predicts persistence and grades, though she stresses rules have limits: belonging will not keep someone in a field that does not pay for groceries.
• Belonging on a team tracks with reported productivity. In her study of 1,282 developers, those on teams with a high sense of belonging and time to learn reported being more productive over the last month and rated their teams more effective. She says the lone-genius, 10x-developer stereotype holds grains of truth but, over-activated as a team norm, leads teams in the wrong direction.
• Code review anxiety competes with code for working memory. Hicks says anxious reviewers avoid or mentally check out of reviews, because worrying about colleagues uses the same cognitive resources as problem solving. Self-efficacy, the belief you will get past the friction, predicted productivity in her research, and she favors brief, repeated exercises such as her lab's workbook that build a "virtuous cycle" rather than a one-off intervention.
• Hackathons let people safely try on new roles. Pluralsight engineers asked her lab to measure their hackathon's value, and the lab found that people on supportive teams were more willing to try new skills afterward, whether or not product features resulted. She argues the temporary team lowers the stakes of experimenting with a new identity in front of a manager.
• AI skill threat is common, and team culture halves it. Surveying over 2,000 developers and 795 engineering managers, she found roughly 43% to 45% agreed they would not keep up or be judged fairly in an AI-coding future, and being a manager did not protect against it. Those on teams with belonging and learning cultures had about half the threat; she says cognitive offloading can be good or bad, the answer is "never going to be in the tool", and she worries tool builders are not bringing in learning scientists.