Souk's Notes - Unofficial Summaries

2025-01-23 · open the original ↗

• Trump asking OPEC for more oil is why US companies won't drill. He notes Trump did this on his third day in office, as he says he predicted many times. He argues companies do not trust a president who would outsource energy to unfriendly countries, citing Trump saying he doesn't "give a damn" if oil companies drill themselves out of business.
• Oil workers should prepare for OPEC eventually caving to Trump. He expects OPEC to refuse the first few times, but says that if this administration is like the last, it will eventually give in. He tells industry workers who voted for Trump against his October warning to save every dollar and prepare for long-term unemployment, for themselves or their families.

2025-01-23 · open the original ↗

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2025-01-23 · open the original ↗

• Tailscale's value is a control plane layered over WireGuard. WireGuard (ChaCha20 encryption, public/private key authentication) carries the data; Tailscale's coordination server tracks every endpoint and builds direct point-to-point links, giving a mesh instead of a hub-and-spoke VPN where remote users talking to each other must cross the main office, adding latency and a single point of failure.
• Devices behind double NAT connect without any port forwarding. Where a direct link fails, as behind Starlink, traffic goes through a DERP relay server using STUN and ICE; the host says the relay only forwards packets and cannot decrypt them, and running your own DERP server is possible but "totally unnecessary".
• The free tier covers a typical household. Tailscale counts machines and users separately, and the free account includes 100 machines; the host's example is three family members across phones, tablets and laptops, and he says he has not outgrown it. MagicDNS names every device as machine name dot tailnet name, avoiding a separately maintained DNS server.
• A pfSense router can share its LAN or internet connection. On a Netgate 4200 behind double NAT, he enrolls the router with a pre-authentication key, advertises 192.168.1.0/24 as a subnet route, and reaches a CasaOS box at 192.168.1.102 from an iPhone on LTE. Advertised subnets and exit nodes both need admin approval in the dashboard; users then pick an exit node to tunnel all their traffic.
• Access controls are powerful but written in JSON. Tailscale denies by default, but its starting rule allows any source to any destination, so most home users need not touch it. Rules are directional yet stateful (return traffic is allowed), can reference users, groups and devices rather than IPs, and are pushed to and enforced on each client.

2025-01-23 · open the original ↗

• Many problems brought to generative AI do not need it. Aishwarya, a generative AI tech lead at AWS's Generative AI Innovation Center, calls AI therapy too far-fetched because it is subjective and models are stateless and expensive to retrain, while simple step-by-step workflows only need an integration. Generative AI adds most value in objective knowledge search and retrieval; of 10 people talking about AI, perhaps two or three know anything beyond agents, and many asking for agents do not know what RAG is.
• An AI engineer translates business problems and metrics; tools are secondary. The role is to turn a business problem into an AI problem and its evaluation metrics into business metrics, much like software engineering but handling nondeterminism such as hallucination and sycophancy. Whether LangGraph or CrewAI is best "doesn't really matter": frameworks get replaced, and Aishwarya's team uses Bedrock agents.
• Learn prompting, RAG, agents and fine-tuning before building projects. Aishwarya's route is to use LLMs daily, follow the mind map in Aishwarya's own free 10-week GitHub course and DeepLearning.AI's short courses, then build projects that automate your own repetitive pain points, such as the agent that compiles a monthly paper list. Estimates: two or three weeks of theory and one or two months of projects, three or four months in all, and about a year to follow research.
• Evaluation is the hardest part, technically and operationally. Aishwarya's workflow runs from cost, latency and performance constraints to an evaluation set built from logs or synthetic data with subject matter experts, then RAG, agents for mixed sources, fine-tuning if needed, and monitoring with LLM judges. BLEU and ROUGE no longer work; LLM judges vary widely on 1-to-10 scales and favour longer answers, so use binary metrics and check that a judge's reasoning matches the human's.
• When an AI application breaks, go back to the data. Aishwarya says more time goes into the data feeding models than into the models, because real-world data has annotation issues and conflicting answers, and no model can fix garbage in. Personal picks at the time: Claude Sonnet 3.5 for code in Cursor or Codeium, ChatGPT for creative writing, NotebookLM for research papers.

2025-01-23 · open the original ↗

• Dev containers standardize a whole dev environment in one config file. Ayers says containers, unlike VMs, share a Linux kernel and so are small, fast and immutable; the dev container spec at containers.dev turns that into a config that names a language base (Python, .NET, Java, Go), extensions, ports and tools such as the Azure CLI, supported by VS Code, JetBrains and GitHub. He argues it makes onboarding "a couple of clicks" instead of weeks of requisitioning and installing software.
• Checking the dev container into the repo ends version conflicts. Ayers says the config lives in the repo's .devcontainer folder, so everyone gets the same tool versions, extensions and formatting settings without NVM or virtual-environment workarounds, and VS Code offers to open it automatically. Nearly all his own repos have one, and he cites CPython and Home Assistant as major projects that use them.
• Codespaces is the same dev container, hosted in the cloud. Ayers says opening a repo in a Codespace spins up a dev container and connects browser or local VS Code to it, with port forwarding and debugging, which removes the local dev container's shortcoming of needing Docker and a powerful machine. GitHub accounts get free monthly minutes, and sizes range from 2 to 16 cores, so a Chromebook can drive a 16-core, 64 GB machine for ML work.
• Without a config you get a universal image; with one, full control. Ayers says Codespaces defaults to a universal image with recent Java, Node, Python, .NET, Go and Rust, but will use a repo's own devcontainer.json. VS Code's "add dev container files" command picks a base (Ubuntu, Alpine), layers tools such as kubectl or Terraform, and sets lifecycle hooks on start or rebuild; Dependabot can flag outdated dev container components.
• Start with Microsoft's "try" sample repos and the free docs. Ayers recommends installing the VS Code Dev Containers extension and cloning the "try dev containers" repos (Rust, Java and others), where breakpoints in the container hit from the desktop browser. He points to the VS Code and GitHub Codespaces documentation, containers.dev for the spec and integrations, and JetBrains' own docs.

2025-01-23 · open the original ↗

• Dictating a day's jumbled tasks to a chatbot yields an organised plan. After the school run Azhar records up to 10 minutes of mixed tasks in Claude's audio mode (a speech, car insurance, a parking ticket, a Stripe refund), and on reaching his desk 15 minutes later finds a sorted to-do list, a drafted appeal letter and ordered speech notes. He calls Claude 3.5 Sonnet good enough to change his day.
• The missing piece is turning plans into actions. For now he copies and pastes; he says AI companies are promising that step "this year". He expects hundreds or thousands of agents working for each person and wants a "chief of staff" supervisor agent that takes unstructured input, doubting Apple will build it.
• His multi-agent debate succeeds about half the time. He defines a historian of technology, an investor and a cynic, run on o1 and at least two Claude 3.5 models, with an orchestrator making them argue. A run takes five or six minutes and costs about 20 cents, and fails about half the time, which he says matches human teams on open-ended research. He builds it in Wordware, disclosing he is an investor, or Lindy.
• Learning AI means working close to the API. Azhar says calling models directly lets you set temperature (sensible but creative, around 1, for an evaluator), which third-party interfaces hide. He also uses Gemini Advanced research, NotebookLM, and an email-drafting tool he has invested in; the host describes using You.com's research mode alongside Claude 3.5 Sonnet.

2025-01-22 · open the original ↗

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2025-01-22 · open the original ↗

• Who is the user: the user is the hero, you are Yoda. Smith, a software engineer of about three years at Microsoft building an internal design system's component library, says his users are the developers consuming it, not end customers, and that DX and CX are subsets of UX that non-designers wrongly treat as someone else's problem. Teams cast themselves as Luke Skywalker and showcase their product, when their job is to help the user succeed; Giard offers his own early failure of building data-entry forms that mirrored relational tables, so users had to learn referential integrity to delete a customer.
• State what they want and how you help, repeatedly. Smith's second and third questions overlap: users skimming search or ChatGPT results are in "fast survival mode", so any ambiguity in what you solve gets missed and the message has to be broadcast "over and over". Whether they adopt the product, he says, depends on how well you articulate their success story with them at the centre.
• Question four asks what you need back from the user. Smith says it is not the usage instructions but the business outcome the builder needs (a purchase, an email sign-up, clean data, acknowledgement that secures funding), and conflating it with what the user wants leads to frustrated experiences. His example is a grocery store such as Kroger trading loyalty-card discounts for customer data; Giard frames the split as question two being the user's needs and question four the provider's.
• Question five: incentives are sometimes the experience, sometimes explicit. A clean way to solve the problem can be incentive enough, as with Stripe's developer experience, Smith says, but asking for money or personal information usually needs an explicit exchange like the loyalty card.
• Expect to get the answers wrong first, then iterate. Smith says elaborate personas from UX workshops are usually wrong, which is fine if you ship an MVP, learn, and re-ask the questions with each release; low engagement signals a wrong problem or wrong users. Users also prescribe solutions (as in the "very apocryphal" Henry Ford "faster horse" line), and he tells a story of an office that answered slow-elevator complaints by installing mirrors rather than a costly faster motor.

2025-01-22 · open the original ↗

• Public clouds grew out of platforms built for companies' own services. Russinovich traces Azure to the mobile shift, Ray Ozzie's 2005 services memo, and Project Red Dog, formed as he joined Microsoft in 2006, which Dave Cutler joined; AWS launched EC2 and S3 a year after the memo for Amazon's own needs. He says opening a platform to strangers adds most of the work, because you cannot trust the public as you trust your own staff.
• Cloud economics come from pooling and self-service. Enterprises provision for peak load and leave 30% to 60% headroom idle, while a multi-tenant cloud oversubscribes statistically because customers peak at different times, running close to 100% utilization. The other gain is agility, such as Scott Guthrie's early goal of a website in 90 seconds.
• Azure assumes "hostile multi-tenancy", isolated by virtualization. Russinovich coined the term against friendly enterprise multi-tenancy, treating the hypervisor's small trusted computing base as the security boundary, with networks virtualized too. Placement is handled by an allocator he co-authored a paper on (rendered "protein"), which packs VMs to avoid fragmenting servers and uses ML to predict arrivals; he puts the fleet's out-for-repair share at about 3% or lower.
• Azure launched as Windows-only PaaS; sales forced IaaS. Announced at PDC 2008 and commercially launched in February 2010, it required apps rewritten as cloud services; in 2011 Microsoft's head of sales told Amitabh Srivastava he would stop selling it without IaaS. Russinovich and Corey Sanders wrote the plan, IaaS shipped about a year later with Linux VMs, and he says Azure now likely runs more Linux than Windows VMs.
• Most developers need not learn the layers beneath them. Russinovich cites falling sales of the Inside Windows books, from about 250,000 copies for the 1996 edition to 75,000, 50,000 and 40,000 for later ones, as abstraction making internals specialist knowledge. Hanselman's advice is to learn one layer below your comfort zone.

2025-01-22 · open the original ↗

• Linear forecasts ignore how things actually grow. Azhar says business plans still show fixed increments, though growth follows a logistic S-curve with an exponential phase; "nothing is linear except our birthdays". He points to compound interest and late pension saving, and says Bay Area tech internalised Moore's Law. Listeners have lived through exponentials in phone upgrades and data lakes growing from a gigabyte to a petabyte.
• Model growth as compounding percentages with feedback loops. He says he has never used linear increments in more than 20 years of planning, and builds in feedback loops such as Intel chips enabling Windows features that drove upgrades. A linear multi-year S3 storage budget, he argues, is the extreme assumption, even if a realistic one alarms the CFO.

2025-01-22 · open the original ↗

• No drilling sites exist in ANWR because nobody wants to drill. An activist is travelling to film planned drilling sites with GPS coordinates and CGI. He says there are no such coordinates, calls ANWR a political football, and notes the most recent lease sale drew not a single bid for a single acre.
• Nobody knows where ANWR's oil is, or whether it exists. He says the only data is 1980s seismic, which he calls junk that no operator would use to pick a site. He also says the source rock behind Prudhoe Bay's production does not extend from the coastal plain into ANWR.
• Any future interest would centre on land bordering the northernmost section. He says the area he calls Section 102, technically excluded from ANWR, is where operators would look first. He stresses he does not expect drilling there either, and has no objection to the trip.

2025-01-21 · open the original ↗

• The host is selling surplus office gear through the Crosstalk store. Prices include shipping: an Anker 8-in-1 MagSafe charging station for $40 (from a $49 grab bag; valued at $69), and a Wyze Cam v3 bundle for $85 (three cameras, two used, plus a spotlight, socket lamp and solar panel; about $163 new).
• An unopened Arista Wi-Fi 6 access point goes cheaper than eBay. The Arista C-200 is a 2x2 MIMO Wi-Fi 6 AP with a single gigabit port, still in its packaging; he puts eBay at about $45 plus shipping and asks $35 shipped. The items are listed under "Tech Sale Tuesdays" in the store on the Crosstalk Solutions website.

2025-01-21 · open the original ↗

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2025-01-21 · open the original ↗

• Assuming exponential compute growth stops is the more radical bet. Azhar calculates world computing capacity has grown about 65% a year compounded for 52 or 53 years since 1972, and his baseline for 10 to 30 years out is to extend that line. He grants the turkey-before-Thanksgiving objection but argues the curve rests on processes (parallelism, interconnects, financial incentive, soon AI help) rather than on shrinking transistors alone.
• Linear forecasts are the extreme assumption in planning. He says he has modelled growth as compounding percentages with feedback loops (Intel chips enabling Windows features driving upgrades) for more than 20 years, and that a linear S3 storage budget contradicts history. Most real growth follows a logistic S-curve; "nothing is linear except our birthdays".
• AI-native companies are opening an "exponential gap". Azhar says fast-growing AI companies reach $30 million annualized revenue in about 20 months against about 60 for SaaS, and that Anthropic reached a billion dollars in sales in
  2024. His advice is to build "the muscle of working with AI" on APIs and semi-finished tools, neither finished SaaS workflows nor your own foundation models.
• Energy is turning from a commodity into a cheapening technology. He says fossil energy has not got cheaper in 200 years while solar panel costs fell by, he thinks, 99% over 20 years, with batteries on a similar curve, and likens the shift to telecoms giving way to the internet. He cites an Oxford paper from about two years earlier finding a fully renewable system cheaper to run, and puts the energy transition at a couple of decades and cheap carbon capture and desalination further out.
• AI is needed to keep knowledge growing as population peaks. Azhar expects human population to peak in 60 or 70 years, after which fewer people can work on problems. On jobs he says there is "no solid ground": citing David Autor's finding that 60% of today's US jobs did not exist in 1940, he expects most jobs to change, everyone's work to become more "boss-like" directing AI, and skilling up as the only preparation.

2025-01-21 · open the original ↗

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2025-01-21 · open the original ↗

• The order lets Washington force energy projects through, aimed at California. He reads the national energy emergency order as invoking federal eminent domain and, if necessary, the Defense Production Act, which he says could force companies to build pipelines, refineries or wells. He reads its references to the West Coast and to "dangerous state and local policies" as a message to Gavin Newsom.
• The emergency is used to bypass environmental law. He says the order claims exceptions to the Clean Water Act, the Endangered Species Act, the Rivers and Harbors Act, and the Marine Protection, Research and Sanctuaries Act. A less alarming provision makes E15 fuel available year-round and nationwide.
• The order's definition of energy deliberately leaves out wind and solar. It lists crude oil, natural gas, lease condensates, natural gas liquids, refined products, uranium, coal, biofuels, geothermal heat, flowing water and critical minerals. He says the omission is intentional.
• The order sets no goals or targets to end the emergency. He notes agency heads are told to produce an unspecified plan within 30 days, and to report back even without one. He finds no production figures, no definition of when the emergency ends, and nothing about "drill, baby, drill".

2025-01-21 · open the original ↗

• Tariffs cannot come close to replacing payroll taxes. Economist Christopher Clarke finds payroll taxes raised about $1.2 trillion of roughly $4.47 trillion in federal revenue; a blanket 25% tariff on $4.1 trillion of imports yields $1 trillion only before substitution and retaliation. He cites a Journal of the European Economic Association estimate that tariffs could replace perhaps 2–5% of US revenue, calling tariffs worse than a sales tax.
• Trump's tariffs would slow growth but likely cause only a modest recession. Clarke expects at most about one point of extra inflation, say 2.9% to 3.8%, which the Fed could handle by holding rates or raising half a point. His forecast range is roughly −2% to +2% GDP, because a $30 trillion economy imports comparatively little; the host had feared worse.
• The Fed, more than supply shocks, drove much of the recent inflation. For 2021–2023, Clarke cites a new Fed paper using deviations from the Taylor rule: rates stayed too low too long and added perhaps 3–5 points, versus 2–3 points from the energy shock in the 9% peak. The host blames Musk and Thiel for the March 2023 Silicon Valley Bank run; Clarke says the bank's fundamentals were already flawed.
• Mass deportations threaten growth that immigration has been carrying. Clarke says 3% real growth depended on millions of new workers as US births fall to about 1.67 per woman. He cites research on the 1930s repatriation of about 400,000 Mexican-Americans, a quarter of that population, which hurt native employment by 1940, partly through lost entrepreneurship.
• Institutional limits will blunt Trump's more radical aims, the guest expects. Clarke predicts Trump's approval will tank within a year, and says narrow majorities and the filibuster, which senators will not abandon, block steps such as abolishing the Education Department. He expects Musk to wield think-tank-style influence, having already won the H-1B and budget fights; the host fears a broader rightward cultural shift.

2025-01-20 · open the original ↗

• Johnson's account of Biden's executive order fails on dates. Speaker Mike Johnson is quoted saying his first meeting with Biden scared him because Biden did not know what was in his own executive order. He sets out that the first meeting was on October 26, 2023, while the order in question was signed on January 26, 2024, three months later, and argues this proves Johnson was lying.

2025-01-20 · open the original ↗

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2025-01-20 · open the original ↗

• AWS's generative AI stack runs from plug-and-play to fully granular. The guest (Eremenko, in episode 853) says Amazon Q offers no choice or customisation of the base model. Q Business is effectively RAG across S3, RDS, Kendra, Gmail, Dropbox, Slack, Zendesk, Jira and Salesforce, with an option to fall back on the model's own knowledge. Q Developer is a coding assistant in JetBrains, VS Code and the CLI, and an AWS-account assistant.
• The middle layer suits most; the granular one needs technical staff. He describes Bedrock as pay-as-you-go access to proprietary and open-source models, with prompting, RAG and agents. SageMaker covers the whole machine learning pipeline with finer customisation and deployment, but needs more technical staff or a partner. The guests disclose no Azure or GCP experience, after over two and a half years on AWS.
• A low-code tool outperformed his hand-tuned models, he says. The second guest (de Ponteves) reports that SageMaker Canvas built, trained and tuned a model in a few clicks and five minutes that outperformed every model he had spent hours tuning on his usual benchmark dataset. He also praises Data Wrangler for preprocessing and JumpStart for vision and NLP foundation models beyond LLMs.

2025-01-19 · open the original ↗

• Instruction-based fine-tuning adds an explicit instruction to each example. The guest (de Ponteves, in episode 853) fine-tuned an Amazon Titan model on niche topics with an instruction to answer in two or three words ("a dog" to which pet is more attention-seeking), and says the result gave concise, straight-to-the-point answers. The difference from domain-specific fine-tuning, he says, is an extra dataset column carrying the instruction. The host notes his former company Nebula instead did domain-specific fine-tuning of Llama models with LoRA.

2025-01-18 · open the original ↗

• Data science pay turns on several distinct factors. The speakers list industry, seniority (IC1, IC2, IC3, with more experience paid more), the area of the organization, location (the Bay Area pays more), performance, and specialized skills in demand. They add the macroeconomic situation, citing 2021's high demand and low supply, and a company's own need: a role critical to the business that must be filled fast gets paid more.

2025-01-18 · open the original ↗

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2025-01-18 · open the original ↗

• Start with cost, modality and the customisation the model supports. The guest (Eremenko, in episode 853) says pricing differs between models, that input and output types (text, image, video) rule models in or out at once, and that you need to know which customisation methods you need before checking which models support them.
• Match inference options and latency to how the model is used. He contrasts real-time uses with no delay (an in-game feature, a natural voice conversation) with asynchronous answers in five minutes and background batch jobs.
• Benchmarks, language and size trade performance against price. He notes that leaderboards score the base model before you customise it, that average performance at a lower price can suffice where accuracy is not business-critical, and that small language models are gaining ground.
• Scalability, compliance, licensing and energy are easily overlooked. He asks how a deployment serving 10,000 users grows to 100,000, flags growing AI regulation and model licences, and suggests weighing a provider's chips and energy use in training and inference.
• In practice, dataset format decided the model. The second guest (de Ponteves) says he chose an Amazon Titan model for one fine-tuning experiment and a Llama model for a medical chatbot because each matched his data's format, and mentions Amazon's just-released Nova models.

2025-01-18 · open the original ↗

• The claim about Iranian exports under Trump is only half right. A clip says Iran was down to 100,000 barrels a day of exports when Trump left office and is now near 1.7 million. He agrees on the current 1.7 million but says the Trump-era low was about 400,000.
• US gasoline prices, not who is president, set sanctions enforcement. Sanctions have stood since the Obama administration, and he argues enforcement tightens when US gas prices are low because Americans will not pay at the pump to punish Iran. He reads the history this way: Obama halved exports to force the nuclear deal, exports recovered as a reward, stayed high in Trump's first two years, then fell once the 2018 oil crash allowed enforcement.

2025-01-17 · open the original ↗

• The Switch Flex 2.5G PoE is a compact, powered-uplink 2.5G switch. It is $199 MSRP and available from January 16, with eight 2.5 Gbps PoE+ ports and a 10 Gbps uplink on either RJ45 or SFP+; it is a little larger than the host expected, and comes with wall-mount screws, a bracket and a levelling template.
• The PoE it delivers depends on the PoE powering it. Powered through the 10 Gbps RJ45 port, it shares 12 W across its ports when fed PoE+, 37 W on PoE++ and 57 W on what Ubiquiti calls PoE+++. The host lays out the standards: 802.3af up to 12.5 W, 802.3at up to 25.5 W, 802.3bt Type 3 up to 51 W and Type 4 up to 90 W.
• The host thinks "PoE+++" branding will not scale. It is his term for 802.3bt Type 4, used to tell it apart from Type 3; he argues you cannot keep adding pluses, and that "PoE++ Type 3 and Type 4" would have been safer if a later standard ends up called PoE+++.
• Full power and full bandwidth together mean buying more hardware. As of recording, only the Enterprise Campus 24 PoE and Enterprise Campus 48 PoE can feed it PoE+++, and both are marked "coming soon". The alternatives are a $29 PoE++ injector, which limits the uplink to 1 Gbps, or a $79 210 W AC adapter.
• On his own bench, the host accepts a bandwidth bottleneck for now. Replacing his Lite 8 PoE, he feeds it at 2.5 Gbps from a 2.5G PoE++ port on his Pro Max 16 PoE, since his Enterprise XG has no PoE, then chains a $49 Flex Mini 2.5G (five 2.5G ports, 802.3af-powered) off it. It negotiated at 2.5 Gbps, and adopting and setting port VLANs took about ten minutes.

2025-01-17 · open the original ↗

• Presidential immunity could be what keeps TikTok from being banned. He asks what would stop a president, held by the Supreme Court to have full immunity for official acts, from simply declining to enforce the ban, and who could sue him. He speculates that TikTok users who feared that immunity would not mind the president using it this way, so long as they kept the app.

2025-01-17 · open the original ↗

• People who say government does nothing rely on it all day. In a satirical monologue he walks through a day touched by regulated power, municipal water, the FCC, NASA satellites, the USDA, the FDA, NHTSA, transportation departments, EPA fuel standards, the Federal Reserve, the Postal Service, public schools, building codes, police and DARPA's internet, ending with a Facebook post that government can't do anything right.

2025-01-17 · open the original ↗

• Intelligence began with physics and chemistry storing information. The host relays Ray Kurzweil's first epoch from The Singularity Is Nearer: atoms formed a few hundred thousand years after the Big Bang, then molecules, with carbon's stable chains allowing elaborate information. Self-replicating RNA, though not alive, let natural selection act. The host adds his own awe that physical constants are fine-tuned enough for this.
• Biological life is the second epoch. Per Kurzweil, RNA learned to make proteins and became trapped in fatty-acid bubbles to form protocells. Selection for more stable membranes and storage, eventually double-helix DNA, produced single-celled and then multicellular organisms several billion years ago.
• Technology, beginning a few thousand years ago, is the fourth epoch. Humans began storing information outside their brains, from clay tablets to solid-state drives, and now machine learning that perceives and reasons. The host forecasts that simply scaling parameters, inference compute and data will keep machines passing humans on harder tasks, and that within a few years scientific discovery may be led more by machines than humans.
• We are entering the fifth epoch, merging brains with computers. Today brain-computer interfaces are implanted mainly for paralysis or motor loss after spinal injury or stroke. Kurzweil expects ordinary brains to be augmented over coming decades, vastly expanding memory and abstract thought.
• The sixth epoch turns the cosmos into a substrate for computation. The host calls it Kurzweil's most speculative idea: intelligence spreads beyond Earth and restructures matter, limited only by the speed of light and quantum mechanics, until individual intelligences merge. He says it may be wild conjecture, but that Kurzweil has been prescient about AI's pace.

2025-01-17 · open the original ↗

• The US jails more people than China because China executes more. Answering commenters he calls defenders of the Chinese government, he concedes the US imprisons more people but argues China executes as many in a day as the US does in a year, often for minor crimes, and points to Amnesty International on China's death penalty. The piece has no energy content.

2025-01-16 · open the original ↗

• Quantum computing will be hybrid with classical compute and AI. Svore, a technical fellow and VP of advanced quantum development for Azure Quantum, describes sending a subroutine to a quantum processor and feeding its results back, including as training data no classical machine can produce. She says Microsoft has demonstrated an end-to-end workflow using HPC pre-processing, logical qubits and an AI model.
• The best targets are low-input, low-output, compute-heavy problems. She points to chemistry and materials science, where accurate modelling of strongly correlated electrons matters, with aims such as carbon capture and cheaper, cleaner fertilizer catalysts. Hanselman compares the processor to GPUs, FPGAs and NPUs as a complement rather than a replacement.
• Physical qubits are too noisy, so logical qubits are built over them. Today's qubits fail about once per 1,000 operations, against the one in 10 million or 100 million needed; Microsoft's "qubit virtualization" uses error correction (Hanselman likens it to ECC memory). With Atom Computing it made 20 logical qubits from 80 physical ones, then 28 logical qubits that beat 28 physical ones, shown at Ignite.
• "Quantum advantage" means solving useful problems, not academic benchmarks. Svore prefers the term to "quantum supremacy": just over 50 qubits can beat classical machines on an academic problem, but useful science such as quantum magnet models needs at least 100 highly reliable qubits. The roadmap runs to "level two" resilient computing, then about 1,000 logical qubits needing upward of a million physical ones.
• Scaling is limited by control, which topological qubits should ease. Qubits cannot simply be stamped out by the million because controlling them adds noise, including heat from the control lines. Alongside the neutral-atom machine co-built with Atom Computing and work with Quantinuum, Microsoft is building its own topological-qubit machine, which she says is fast, digitally controlled, and small enough for a million to fit on a credit-card chip.

2025-01-16 · open the original ↗

• The foam insert fits a Starlink Mini into a Milwaukee Packout well. It fits "like a glove", with a notch for lifting the dish out and space for cables and accessories, so the original box can go; but the host had to remove his Striker mount first and would like a version sized for it.
• The host likes the idea but calls the price crazy. The insert costs $90 against about $80 for the Packout itself, and he invites anyone who can make such inserts more cheaply to contact him, wanting one under $40 and seeing room for versions for other mounts, DeWalt cases and Pelican cases.

2025-01-16 · open the original ↗

• Long data careers rest on learning, network and core excellence. Manisha, a data science leader at Google and founder of the career platform Prep Vector, says the field has changed so much in 12 years that learning must be a habit. Relationships matter because managers move and hire, and there is "no workaround" for being extremely good at the core job; a host adds that your network depends on being remembered for good work.
• Leadership does not wait for a manager title. Manisha separates leading from managing: anyone can fix what is broken, and future leaders show in small signals such as running meeting agendas, writing documentation, following up proactively, and feedback from cross-functional partners. The IC and manager tracks are parallel, with the difference being looking inward at your own work versus outward at other people's success.
• Ask for the promotion early, and be deserving of it. A co-host says few people ask, perhaps 5%, especially women, and that promotions are a lagging indicator of work already done at the next level. Shane missed a promotion at PwC despite five-out-of-five reviews because the partner needed to hear six months ahead; Manisha was once skipped too, and a friend's line was that "the crying baby gets the most attention".
• Candidates lack industry perspective, not theory. Manisha advises one base resume tweaked in about 10 minutes to each job description, and impact stated in business terms rather than model accuracy, since nobody is promoted on accuracy. Career switchers should research job descriptions (A/B testing was the gap Manisha found coming from healthcare consulting), lean on transferable skills such as stakeholder management, and close the gaps.
• The underrated skill is talking at different altitudes. Manisha's answer to a listener's question is zooming in on detail with a research team and out to OKRs and business impact with a VP, on the same project. Shane names prioritization and saying no, judged against stakeholders' goals; a co-host names creativity in data storytelling.

2025-01-16 · open the original ↗

• The first three lifecycle steps are the labs' job, not yours. The guest (Eremenko, in episode 853) says data preparation, architecture selection and pre-training, which costs "hundreds of millions of dollars", are done by companies like Meta, Anthropic, Google and OpenAI. He adds that businesses repeating them would make global warming much worse.
• Fine-tuning narrows a general model to proprietary data. He gives examples of medical journals, legal data, or ten years of customer-service call transcripts. The model learns specific terms and style the way a fluent speaker learns a specialist vocabulary.
• Evaluate against business metrics, not just technical scores. He says technical scores like BLEU, ROUGE and BERTScore are not enough, and a model must also be tested on the business tasks it is meant to do.
• Deployment, monitoring and iteration form an ongoing loop. He explains an endpoint as just a URL with parameters, says models degrade through data and model drift like a car needing maintenance, and says changes in the business, in customer expectations or in legislation send you back through steps four to eight.
• A single AWS service covers most of the lifecycle. The second guest (de Ponteves) says Amazon Bedrock handles most steps except pre-training, with data prep done in other AWS services. His students fine-tuned a Llama model on a Hugging Face medical-terms dataset in "dozens of minutes" and got a chatbot that could discuss advanced terms. The host separately notes that 2017 was the transformer paper, and ChatGPT in 2022 was GPT-3 with RLHF.

2025-01-15 · open the original ↗

• His October call for higher natural gas prices has played out. He replays his October forecast that oil and gasoline would be cheaper in 2025 but natural gas would face upward pressure. He gives natural gas at about $2.30 when he made it and roughly $4.10 now (the captions render it as "$41").
• Flat production against rising demand and exports is drawing inventories. Low prices led production to dip and then flatten while demand kept climbing, and new LNG export terminals are sending more gas abroad, lowering inventories and raising prices. A cold winter in producing regions is also freezing wells, cutting output just as consumption rises.

2025-01-15 · open the original ↗

• A prestigious degree stops mattering once the interview starts. The speakers grant that Harvard is looked on more favourably than a community college, but say the interview is "purely based on performance", and seven or eight years in nobody cares where you studied. Shane notes having gone to community college rather than Harvard; another speaker has clients from Harvard who cannot get the job, and they conclude that applied experience counts for more than what you learned.

2025-01-15 · open the original ↗

• A foundation model is a pre-baked base layer businesses rent and customise. The guest (Eremenko, in episode 853) likens it to a cake's sponge layer, baked by well-funded labs such as Anthropic, Meta and Mistral, to which a business adds its own toppings for its use case. He says the analogy itself came from ChatGPT.
• Foundation models are broader than large language models. The host offers a Venn diagram in which LLMs sit inside foundation models alongside vision models of the kind a Waymo car relies on, and the guest agrees, adding image and video models.

2025-01-15 · open the original ↗

• US energy surplus grows because production rises while consumption stays flat. Showing a production chart going back to about 1950, he says output from oil, gas, wind and solar keeps rising, while consumption flattened about 25 years ago and is slightly lower now than 20 years ago.
• Efficiency matters as much as production for energy independence. He credits appliance efficiency, using a modern fridge against a 1960s one: over the last 25 years the US added 40 million people, 40 million cars and 20 million homes — roughly a California — yet energy consumption has held steady.

2025-01-14 · open the original ↗

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

2025-01-14 · open the original ↗

• Salary secrecy benefits only the employer. The speakers say tech compensation is widely misunderstood because consistent information is scarce and talking about pay is taboo. An employer that can pay a good performer less has no reason to pay more, so knowing the baselines is the only way to help yourself.

2025-01-14 · open the original ↗

• Businesses should customise foundation models, not build them. Eremenko frames a foundation model as a cake's base layer that big labs bake and businesses rent and top. Of his eight lifecycle steps, the first three (data prep, architecture, pre-training costing "hundreds of millions of dollars") are done for you; a business handles fine-tuning, evaluation, deployment to an endpoint, monitoring for drift, and iteration.
• Model choice turns on many factors, and in practice data format. Eremenko lists twelve: cost, modality, customisation options, inference options, latency, architecture, benchmarks, language, size, scalability, compliance/licensing and environmental impact. De Ponteves says his own picks (an Amazon Titan model, a Llama model) came down to which fit his fine-tuning dataset's format. The host relays Andrew Ng's advice to ignore cost while prototyping; Eremenko objects for enterprises certain to roll out.
• Domain-specific fine-tuning is the business default. Eremenko ranks it the most common training-time method, ahead of instruction-based fine-tuning (which de Ponteves describes as an extra instruction column in the dataset), RLHF, continued pre-training and training from scratch. De Ponteves reports fine-tuning a Llama model on a Hugging Face medical-terms dataset in "dozens of minutes" to get a medical chatbot.
• Inference-time levers give large gains for little effort. The pair cover temperature (de Ponteves's story generator repeated one story word for word at temperature zero), RAG over a vector store (a French-dessert assistant from one PDF), agents that split tasks into steps so a simpler model can beat a bigger one, and prompt templates, which Eremenko warns can still be hijacked by injected instructions.
• Pick an AWS service by how much control you need. Eremenko calls Amazon Q (Business, Developer) plug-and-play with no model choice, Bedrock the pay-as-you-go middle layer, and SageMaker the most granular. They disclose no Azure or GCP experience; their AWS focus follows their course business's certifications. De Ponteves says SageMaker Canvas beat his hand-tuned models on his benchmark dataset in five minutes.

2025-01-13 · open the original ↗

• Using quantum computers does not require a physics degree. Neukart (Terra Quantum, in episode 851) says physicists are needed only to build chips. He argues the real barrier is meeting superposition and entanglement first as adults, and suggests quantum games for children. For anyone in data science, he calls quantum "a technology like GPUs" that can be added to the stack.
• Regression, classification and LLM hybridisation are fertile ground. He names image classification, chassis-damage inspection on production lines, medical images and self-driving, with LLM hybridisation an active research area with open questions. A model can be trained with a quantum computer and still run inference on a mobile device without one. For learners he points to Terra's TQ42 platform and TQ Academy training.

2025-01-13 · open the original ↗

• Renaming the Gulf of Mexico does not undo offshore drilling bans. A social media creator claimed Trump was renaming it to get around the drilling ban Biden had issued three days earlier. He says bans attach to named lease divisions and boundaries that a new name does not change. The banned division has been off-limits for 30 to 40 years, Trump himself banned it, and Biden's order rests on the same law Trump used; existing producing areas are not banned.
• Cheaper gas does not bring cheaper groceries. After the late-2018 crash he says Trump engineered, low gas prices in 2019 came with higher food inflation every month than in 2018; in 2014, at the start of the biggest oil crash, food prices rose faster than in 2013. He argues higher fuel prices raise food prices, but lower ones never reverse them, since grocery companies keep the savings; only an economic collapse lowers grocery prices.