Souk's Notes - Unofficial Summaries
Scott HanselmanAI-Powered Migration plus Raw Experience with Mike Rousos
synopsis
Mike Rousos, a principal software engineer in customer engagement at Microsoft, ends up holding that AI-assisted migration works only as an incremental mix. Deterministic tools do the known 75%. An LLM handles the unusual remainder. A human validates, and experience still decides how much the tooling helps.
What customer engagement is. His group sits in the product organisation and advises customers on new .NET and Azure tooling without charging and without doing the work for them. Account teams bring cases, and the group picks those with the most to teach the product.
A triple migration. The recent customer was moving 50-plus apps off old .NET Core, .NET Framework and bare metal into App Service Environment, mainly to stop maintaining a data centre. Rousos lists the reasons to upgrade: support (.NET 5 has expired; Framework 4.8 is supported indefinitely), NuGet packages, cross-platform support and hiring. He warns that performance gains require profiling first.
Validation as the bottleneck. Only one app had tests, and the teams upgrading them didn't own them. He says validation takes as long as the upgrade, so tests come first. Hanselman's version is to write tests around whatever he fears breaking, such as his blog's 20-year-old rewrite rules.
Where Copilot fits. The Copilot app modernization tool, generally available about a week before recording, combines static analysis, a knowledge base served over MCP, and an LLM for build errors. It asks for help after three or four failures and stores the developer's diff to reuse. He recommends going step by step, with documentation, then tests, then transformation. His team's "X to Y" template has Copilot write a structured prompt and then run it in a fresh session.
The experience gap. Customers report about 25% gains, mostly from seniors. One junior said "I don't know what to ask it". Hanselman says the qualification for migrating old code is being an old programmer and asks how AI could teach juniors instead. Rousos concedes he has no answer yet.
- Deterministic tooling handles most of a migration; the LLM handles exceptions. Rousos, a principal engineer in Microsoft's customer engagement group, says rules and static analysis cover about 75% of a .NET upgrade, and the LLM fits the rest together when builds or restores fail unexpectedly. He calls each customer's app a "herd of unicorns". Hanselman frames it as keeping deterministic steps deterministic and sending ambiguity to the LLM.
- Validation takes as long as the migration itself. Rousos's recent customer was moving 50-plus apps from old .NET Core, .NET Framework and bare metal to App Service Environment, and only one app had automated tests. Central teams who don't know the apps own the upgrades, so he recommends recording Playwright tests or generating tests first. Hanselman describes writing about 20 tests around his blog's routing before migrating it.
- Upgrade for support, ecosystem and hiring, then profile for speed. .NET 5 is out of support, while .NET Framework 4.8 is supported indefinitely. Rousos cites NuGet packages dropping Framework support and customers who can't hire for Web Forms. Modern .NET raises the performance ceiling, but customers who don't profile have seen no speedup because the same slow SQL query remains.
- Copilot app modernization works stepwise and learns from developer fixes. Rousos says the Visual Studio tool reached general availability about a week earlier and draws on a knowledge base through an MCP server. When stuck after three or four attempts it asks for help and stores the developer's diff locally for later similar errors. The .NET Upgrade Assistant began as his team's prototype for a customer. He advises working incrementally: Copilot writes documentation one day and tests the next, not a one-shot migration.
- Structured prompts beat one-shot requests for code transformation. His team's "X to Y" template has Copilot first write a prompt that includes a task list and success criteria, then run it in a new session. He keeps instruction files reminding Copilot that old Framework apps use MSBuild and packages.config.
- Seniors report 25–50% gains; juniors often report none. At one customer, juniors said Copilot did not make them faster, and one said "I don't know what to ask it". Hanselman reports 25–50% gains on his own roughly 12 hours a week of coding. He ties the junior gap to a paper he is writing with Mark and asks how AI could teach early-career engineers on engagements. Rousos says he has no answer yet.
Crosstalk SolutionsThis Wireless Planning Tool Feels Like Magic!
- Hamina is the professional sweet spot between free tools and Ekahau. The host prices a Nomad survey device at about $1,700 plus $2,340 a year for the Planner Plus and Onsite licence, just over $4,000 the first year; the equivalent Ekahau setup is well over $10,000 (Sidekick 2 about $5,000, licence $5,500, renewals near $2,000 a year). The video is not sponsored, but Hamina gave him the licence free.
- UniFi's free Design Center only suits all-UniFi jobs. Hamina covers dozens of manufacturers and hundreds of access point models (Cisco, Ruckus, TP-Link) and ties in with on-site survey data; a limited free Planner Lite exists. He says it is a tool for professional work, not for planning a home network.
- Floor-plan import auto-detects walls, doors and materials. On a sample floor plan from the TV show The Office, it auto-scaled the plan and found drywall, heavy drywall, wooden doors and an elevator, viewable in 3D; walls can also be drawn by hand from a material library with per-band attenuation values.
- The AI auto-planner overplaced APs but handled 2.4 GHz sensibly. Using TP-Link EAP653 Wi-Fi 6 units, it placed too many access points for the host's taste and he cut them to two, but it had disabled 2.4 GHz on about half and spread the rest across channels 1, 6 and 11. A client simulator shows band changes and roaming as a device walks the floor, and multi-floor layouts account for bleed between floors.
- It also plans cabling and produces a customer-facing bill of materials. He added two 48-port switches with 500 W PoE budgets, an ER707-M2 router (four ports, no PoE), cameras at entrances, PCs and a phone at every desk (he likes two drops per desk); cable trays tidied the runs, the bill of materials showed over 2,500 ft of Ethernet, and customers get a branded live link showing coverage.
Mr. GlobalWhat Made The Office Of President So Corrupt
- Trump's corruption is conducted openly and bragged about. He lists a suit against the DOJ for roughly a quarter billion dollars, privately funded ballroom donations with a secret donor list, a crypto venture he says Trump deregulated on taking office, family deals, and TV networks paying up after lawsuits and licence threats, all within about 10 months.
- Decades of partisan media primed voters to accept it. He argues 30 years of outlets portraying the Bidens, Obamas and Clintons as crime families convinced Republicans every president is corrupt, so they shrug at Trump's version and even appreciate that he does it in the open.
- The scale of tolerance has inverted past scandal standards. He contrasts James Comer spending a year investigating a $10,000 check from Joe Biden to a relative with a sitting president suing for a quarter billion dollars in a case whose outcome his own administration decides.
- He expects successors to copy the model. His reading is that earlier presidents were held back by norms and fear of getting caught; Trump has shown it can be done, so, assuming elections continue, he predicts someone will mimic him because they know they can get away with it.
Dr. Jon KrohnHow to Rewire Your Skills for the Age of AI (with Sheamus McGovern)
- The speed of change is itself the new challenge. McGovern says change always outruns most people's ability to absorb it, which creates opportunity for those who adapt. He cites a study, which he does not fully believe, that skill requirements in AI-exposed jobs turn over by roughly 30-something percent, about every three years, and he repeats the host's line that compute doubles every six months.
- Skills keep expanding like the universe rather than disappearing. From three decades in tech, McGovern argues old skills rarely vanish (COBOL programmers are still needed); there are simply more of them. The host adds that data science has branched into AI engineering, which will itself branch.
- Treat AI as a collaborative partner and look at what is newly possible. McGovern says AI has moved from tool to partner since about 2015 and will either augment or automate work, both of which create opportunity. His example is sales, where lead generation, SDR, account executive, account manager and sales engineer roles can be rolled up, letting one person do far more.
- Most office work is drudgery, so roles shift toward orchestration. He relays the CEO of OpenHands (an open-source successor to OpenDevin) telling him most work today is drudge work, and adds that automation still needs oversight and judgment. Data and AI roles will therefore involve less building models from scratch and more designing workflows, supervision and evaluation.
Loss DogCheck out our new studio!
- This is a studio-reveal clip with no substantive argument. The brief captions direct viewers to join the Lost Dog waitlist at lossdog.com — a promotional call to action, not a claim of substance.
Loss DogThis Will Change Everything... Get on the List.
- A new hire frames joining Lost Dog around Sosnoff's track record. Jeff Joseph, introduced as Lost Dog's chief strategist, cites Tom Sosnoff's two prior companies, Thinkorswim and Tastytrade, both reaching "unicorn status," as the reason the opportunity "wasn't one to pass up."
- The pitch dangles "Lost Dog's Upside" for early signups, undefined. The first 100,000 names on the waitlist are told they'll get to "participate" in this unnamed benefit — a scarcity mechanic rather than a disclosed detail.
Stock Tok$Taking our first Short Position
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Dr. Jon KrohnHow ODSC, the World’s Biggest Data Science Conference, Began (with Sheamus McGovern)
- The conference grew out of free local data-science meetups. ODSC's founder, McGovern, moved from Fidelity's algorithmic trading floor to hedge funds, where NLP use began around 2009–2010, then to NLP startups and a consulting firm. Around 2011–12 he found free meetups gave better talks than paid conferences that amounted to product pitches, so he started a Boston data science meetup that grew into the Boston Data Festival, with a first ticket of $20 in 2013.
- Demand forced a volunteer festival into a full conference. When he tried to give the festival away because it hurt his consulting business, attendees begged him to turn it into a conference; with three staff he booked the Boston Convention Center and charged $50, and in April 2015 found a queue around the block that his partner said meant they had underpriced.
- The founding idea was that open-source tools make anyone a data scientist. McGovern says scikit-learn, pandas and similar tools plus the will to learn were all it took, at a time when the first data-science master's (which he places at Columbia in 2015) had only just appeared. He says attendees came from history, economics, programming and journalism and have gone on to write books and lead AI at large companies, and that ODSC has stayed practitioner-focused rather than chasing size.
Polis PanditSnatching Defeat From the Jaws of Victory
- The Mariners' Game 7 ALCS loss to the Blue Jays, decided by a George Springer home run in the bottom of the 7th, extends a 24-year absence from the World Series. The author frames it as the inverse of a comeback story — "snatching defeat from the jaws of victory" — after leading late in an elimination game the franchise has never won its way past.
- The author's takeaway is to cherish opportunities rather than assume they'll recur. He notes the team's previous near-miss was 24 years earlier, and stresses being ready "when the doors open" again rather than taking any single chance for granted.
- The loss doesn't erase the value of the run itself. Watching the postseason with his mother is presented as a win independent of the outcome on the field — the memory outlasts the result.
AI Analyst LabHow to 10X Data Work with Hex Agentic AI | Ep39
synopsis
This episode of the Data Neighbor podcast interviews Barry, CEO of Hex, which he confirms raised a $70 million Series C a few months earlier. He argues that agents can multiply data work only on top of curated context, and that the data team's job shifts toward building and watching over that context. He ends by warning that no tool, Hex included, fixes a data team that is not invested in partnering with the business.
Why Hex exists. Barry says he started Hex to fix a problem he had himself. In his account, 80% of the effort goes into dashboards, but 80% of the interesting work happens outside them, in notebooks, SQL and spreadsheets, and gets shared as screenshots in decks. Hex moved that work to the cloud, made it collaborative and added native SQL and chart cells. It also infers an execution DAG from the code by static analysis, which he calls an early breakthrough. AI became Hex's main focus a couple of years ago and, he says, close to its sole focus this year.
Product-market fit as a moving target. Barry dates real fit to two or two and a half years in. Early this year he began telling staff "we don't have PMF", despite tens of millions in revenue, because the frontier moves daily and a quick AI hit is easy to copy. What compounds, he argues, is sustained product taste and an engineering team that can build reliable systems. Hex uses Cursor and Claude Code, but he says you still need engineers who understand the system.
The demo. On demo data, Barry asked Threads for the top-performing reps by customer segment. It searched existing projects, semantic models and tables, ran warehouse queries with visible SQL, and returned findings with charts that can be edited. Saving it as a project opened the underlying notebook and its DAG. There the notebook agent built a 12-month forecast, and the app builder published the result. Barry frames this as a "virtuous cycle" in which published work becomes context for the people who self-serve. He then showed semantic models imported from Cube, Snowflake and dbt, or written in Hex as YAML with an agent that suggested additions, validated them and listed the projects that reference the model.
Where data roles go. Barry predicts that agents will take over the stream of quick questions. Data teams will spend more time on deep dives, on curating knowledge and on "insight observability" over the answers other people get. Where the role ends up in 3–5 years, he says, no one knows. New customers, he adds, need context curated before Threads is turned on, and Hex is now embedding closely with them to build it.
Closing advice. Barry says he has told some prospects not to buy Hex. They wanted a chatbot to satisfy business users, and he argues the real problems are people and process, which a tool cannot solve on its own.
- Product-market fit is never settled, so sustained taste is the moat. Barry, Hex's CEO, says Hex took about two to two and a half years to reach fit, and that early this year he began telling staff "we don't have PMF" despite tens of millions in revenue. His reason is that the frontier moves daily and a quick AI hit is not defensible; he credits a friend who is Figma's CEO with the idea that product taste is the moat.
- Hex's agent answers from the workspace's context and leaves a real project. In Barry's demo, Threads takes a plain-language question (top reps by customer segment), searches existing projects, semantic models and tables, and shows the SQL it runs. It returns findings, a table and editable charts, and "save as project" opens the notebook behind them, with a reactive DAG that reruns only dependent cells. The notebook agent then built a 12-month forecast, and the app builder published the result as a data app.
- Published work becomes context that everyone else self-serves on. Barry calls this the "virtuous cycle": the data team works on the "new, novel and gnarly" problems, publishes the results as curated context, and everyone else self-serves on it until they hit a wall and hand the question back. Barry demonstrated semantic models imported from Cube, Snowflake semantic views and dbt, plus models written in Hex as YAML with an agent that proposed additions, validated them and showed the diff and which projects reference the model. Chatting off a dashboard is "next", and having agents flag downstream projects a change would break is something Barry says you "can imagine".
- AI will make data teams curators and partners, not obsolete. Barry predicts agents will be the front line for the "dismal tide" of quick number-pulls. Data people will spend more time on deep dives, organizing knowledge as "librarians" and on what he calls insight observability: watching what answers the rest of the company is getting. Where the role ends up in 3–5 years "no one really knows". He says internal use of Threads has made Hex value its own data team more, and that Hex now gives data hires fewer coding tests.
- A chat-with-your-data tool won't fix an organizational problem. Barry says he has told some prospects not to buy Hex, because they expected a tool to satisfy business users without a data team invested in partnering with them. He argues against a standalone chatbot for the business and a separate tool for the data team, and says much of the fix is people and process. He notes that, as a vendor, his incentive runs the other way.
GCastMastering GitHub Copilot course, Using GitHub Copilot with Python, Part 1 [GCast 206]
- A GitHub Codespaces environment comes ready to run the exercise. Giard opens it from a button in the course instructions (github.com/microsoft, the "Mastering GitHub Copilot for Paired Programming" course); it runs in the browser with the repository's code, VS Code, Python, the VS Code extensions and GitHub Copilot already installed. This episode covers sections one and two of the Python lesson's four.
- Ask mode is the least powerful and safest of Copilot's three modes. Of ask, edit and agent, ask only answers questions or suggests code changes and makes no changes itself; Giard advises staying in it until you need edit or agent mode.
- Copilot chat can explain a project and diagram its workflow. Typing "/" lists built-in commands and "/help" describes them; "/explain" with @workspace, an agent that understands the whole project, described the two endpoints in main.py. Asked in plain English for a workflow diagram, it laid out the browser-to-endpoint flow, which Giard calls useful for inherited code or for explaining code to management or a customer.
- A code comment prompts Copilot to write the code as ghost text. Lighter-coloured suggested code with no line numbers appears under the comment; Tab accepts it and Escape dismisses it. From two comments he got a Pydantic model and a FastAPI POST endpoint, /checksum, deleting extra code Copilot had anticipated but he had not asked for.
- Ctrl+I opens chat that acts only on the highlighted code. It is Cmd+I on a Mac. "/improve" reworked the endpoint and added an import of hashlib, with accept, regenerate or close as the options; explicit instructions then added a hash-type parameter (MD5, SHA1 or SHA-256), a check that the text is not empty with a 1,000-character limit, and a fuller docstring.
Master InvestorHoward Marks: Mag7 doesn't concern me, but rest of S&P500 does - be prudent
- Marks isn't troubled that the Magnificent Seven trade at roughly twice the S&P's 80-year average P/E of 16. With Mag Seven multiples in the 30s and Nvidia near 50x, he views them as justified by exceptional businesses with deep moats and hard-to-cut market share.
- His real concern is the other 493 S&P companies, which he says are overvalued without the same justification. Trading around 19–21 times earnings despite being "far more mortal" than the Mag Seven, he calls the broader market "lofty but not nutty" and suggests conservative investors take some chips off the table.
Crosstalk SolutionsStart Making Money with Your Network Skills Today
- Walking into local businesses won the host more customers than advertising. Before YouTube he tried Google ads, Craigslist and newspaper ads without success; handing out cards in person (a goal of five a day) worked best. He advises listening for pain points rather than pitching, and targeting locally owned businesses, since chains like Walmart or Starbucks have central IT.
- A free wireless survey is the best foot in the door. He suggests leaving a one-page flyer offering a free service, with a survey favoured because it is quick and visual; NetSpot Pro costs $199, or Hamina Onsite for more advanced work. Follow up with a recap email within 24 hours, then send a short proposal restating their problems, with a simple diagram but not enough detail to do it themselves.
- A Wi-Fi 7 Omada restaurant build yields over $300 hardware margin. The video is sponsored by TP-Link. His design uses two EAP772 and one EAP772 Outdoor access points, an ER707-M2 router, a 24-port 2.5 Gbps PoE switch with a 500 W budget and an OC220 controller, with separate VLANs for POS and guests; about $1,520 at MSRP against $1,216 at Elite tier (20% off). He notes the upcoming EAP725 Outdoor (November) would be cheaper for the patio.
- Standardizing on one vendor makes cookie-cutter budget builds profitable. A Wi-Fi 6 gigabit version (ER605, SG2428LP with 16 PoE ports, EAP653 and EAP610 Outdoor, Omada cloud controller) costs the customer about $640 with over $125 margin. He argues one ecosystem means faster setup, shared spares across clients, and that Omada is cheaper than brands with ongoing licence fees.
- Omada partner discounts climb from 10% to 20% by quarterly volume. Authorized distributors give 10% off MSRP; Elite tier adds another 10%, and deal registration allows custom pricing on large bids. TP-Link is offering its Wi-Fi 7 demo kit (ER707-M2, two EAP723s, a 10-port PoE switch, OC220) at 50% off, just under $250.
Dr. Jon Krohn933: Future-Proofing Your Career in the AI Era, feat. Sheamus McGovern
synopsis
Sheamus McGovern, CEO of Open Data Science and founder of the ODSC conferences, ends the conversation with an optimistic case: AI multiplies skills rather than erasing them, so the way to future-proof a career is to keep rewiring, from building models towards orchestration, evaluation and human-centred skills, and to prove ability through visible work rather than credentials. He expects AGI eventually, but not on the timelines being promoted.
From meetups to a conference. McGovern traces ODSC to his move from Fidelity's algorithmic trading floor and hedge funds, where NLP use began around 2009–2010, through startups and consulting. Free Boston meetups around 2011–12 gave better talks than paid vendor conferences, so he founded one; it grew into the Boston Data Festival, with $20 tickets in 2013. When he tried to hand it off, attendees pushed him to make it a conference. The first, at the Boston Convention Center in April 2015 at $50 a ticket, drew a queue around the block. The founding belief was that open-source tools like scikit-learn and pandas let anyone become a data scientist. The conference is now renamed ODSC AI, after a trademark attorney warned against "Open AI", and has shifted towards virtual summits because the field moves too fast between annual events.
A decade of shifting skills. He narrates the arc from scikit-learn, PyTorch and Jupyter, through AlphaGo in 2016, the 2017 transformer paper (whose talk went over his head) and BERT, to ChatGPT in November 2022. After that the work moved from hyperparameter tuning to fine-tuning pre-trained models, and then to agent frameworks.
AI engineering and its offshoots. The host proposes AI engineer as a subset of data scientist that may involve no model training at all. McGovern agrees, though ODSC's nearly nine years of job-description data still show data scientist as the most common title. He expects many sub-roles, singling out AI evaluation engineer, because RAG, graph RAG, agent memory, MCP and multi-agent orchestration are hard to evaluate and companies are keen on it.
Rewiring for speed. McGovern says the pace of change always outruns people's ability to absorb it. He cites, sceptically, a study putting skill turnover in AI-exposed jobs at roughly 30-something percent every three years. Skills never disappear, they multiply. AI is becoming a collaborative partner that augments or automates; one salesperson could absorb roles that are now split among several. Relaying the CEO of OpenHands, he calls most office work drudgery and says automation still needs oversight.
Hiring on capability. McGovern has always favoured skills-based hiring and notes Fidelity, IBM and Google dropping degree requirements. At Cortical Ventures he is a part-time venture partner and head of AI; the firm uses agentic workflows to find founders and treats degrees as one signal only. He tells candidates to show proof (GitHub, posts, talks, volunteering) and hiring managers to look for coachability. Picking up the host's relay of Noam Brown's criticism of outdated intro-AI courses, he faults universities for banning ChatGPT. He also argues that rising CS-graduate unemployment, around 7–8% against 3–4% by his recollection, owes much to big-tech hiring rather than AI alone.
Tiny teams. Startups, which he calls "time machines" for the industry, now build small hybrid teams that lean on AI. He questions their fixation on revenue per employee, citing Lovable and Cursor figures from memory.
A hierarchy for the future. Asked whether cognitive work is no longer safe, McGovern lays out tiers: prompting as table stakes, then understanding LLM limits, keeping current with tools, orchestration, human and domain skills, AI strategy and compliance, and finally learning to learn and foresight. He urges personal branding and deliberately retiring old skills, as he has with coding, and dismisses near-term AGI claims.
- AI engineering is splitting into niches, with evaluation the hottest. The host calls AI engineer a fast-growing subset of data science, built on API calls and agent frameworks rather than model training; McGovern agrees but says that, in the job descriptions ODSC has gathered for almost nine years, data scientist is still the most common role. He predicts more sub-roles, and says an "AI evaluation engineer" who can assess RAG, graph RAG, agents, MCP tools and multi-agent orchestration can command high pay now.
- The pace of change, not obsolescence, is the real threat. McGovern cites a study, which he only half believes, that skills in AI-exposed jobs turn over by roughly 30-something percent about every three years. He argues skills never vanish but multiply "like the universe", and that work is shifting from building models to designing workflows, supervision, evaluation and orchestration, because automation still needs oversight.
- Hire on demonstrated skills; degrees are now one signal among several. He notes Fidelity, IBM, Google and possibly Meta dropped degree requirements, and that at Cortical Ventures, where he is a part-time venture partner and head of AI, a founder's degree is one signal only. Hiring managers should look for portfolios, community contributions, coachability and eagerness to learn, since AI roles emerge faster than degree programmes; he says universities are currently doing CS and data-science graduates a disservice.
- AI lets startups run as tiny, hybrid teams. McGovern says startups preview tomorrow's skills and now build small teams that use AI for legal work and day-long MVPs, though he calls the focus on revenue per employee unhealthy. He cites Lovable at 10 million in revenue in two months with about 15 staff and Cursor at 100 million with 45 people.
- Climb a skills hierarchy and sunset old skills deliberately. His tiers run from table-stakes prompting, through understanding LLM limits and keeping current with vendor tooling, to orchestration, then human, domain and strategy skills, then learning to learn and foresight. He has shifted his own effort from coding to prompt engineering and expects AGI eventually but "not in the timelines people are talking about", likening calling today's AI AGI to calling a skateboard a self-driving car.
Stock Tok$Stock Market Opens WAY UP ⬆️
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Dr. Jon KrohnWhy You Don’t Need to Wait for Quantum Computing to Optimize Your Business (with Jerry Yurchisin)
- NP-hard labels wrongly convince people to wait for quantum. The host relays Dell's global CTO, from episode 887, calling quantum computing an inevitability. Yurchisin says the perception that mixed-integer programming falls in a "super hard" complexity bucket leads people to assume such problems are unsolvable until quantum arrives, and he takes no side on whether quantum is inevitable.
- Supply chains can be optimised now rather than in a quantum future. Because quantum is not a viable way to solve problems today, Yurchisin argues executives should capture savings now with classical mathematical optimization instead of waiting.
- Optimization work today is the groundwork quantum would need anyway. Understanding the business case, stakeholders, buy-in and data connections must happen either way, he says, and an optimal classical solution gives a reliable benchmark for any future quantum proof of concept. What quantum might add is finer granularity (modelling individual workers) or turning 10-minute or hour-long solves into a second or millisecond.
Master InvestorHoward Marks: No intelligent way to assess gold's value
- Marks argues gold's popularity reflects short-term sentiment, not a rational fear trade. Invoking Benjamin Graham's "voting machine" idea, he notes the stock market is at an all-time high and the dollar has been stable for six months, undercutting the common story that investors are fleeing to gold out of fear.
- He says there is no intelligent way to calculate gold's intrinsic value. Unlike a cash-flow-producing asset, he says gold sells for whatever "the buyer will pay and the seller will take," meaning one can only buy it out of fear, aggression, or superstition, never analytically.
GeoHussarThe Netherlands just hit China hard with a takeover
- The Netherlands seized Nexperia from its Chinese owner. It used a 1952 Cold War-era law allowing takeover of any company on national security grounds; the semiconductor maker had been owned by a Chinese entrepreneur since 2019. A Dutch interim manager now runs it, pending a decision on permanent leadership.
- The seizure costs China a rare foothold in Dutch chips. He says China is furious because the Netherlands, through ASML, is the only maker of the lithography machines used to produce semiconductors, so any position inside the Dutch industry was a major win for Beijing.
- The stated driver is Nexperia chips reaching Russia. He says Nexperia semiconductors went into Chinese equipment sold to Russia with dual-use application, which could be stripped down and the chips reused in missiles, drones or other military technology. He calls it a major escalation in the West–China semiconductor war.
- He argues the move is lifted from China's own playbook. He cites GlaxoSmithKline being hit with corruption charges in 2014, staff detained and Chinese subsidiaries forced into sale, plus antitrust and cybersecurity actions against Samsung and Nortel. He says Tesla, Micron, Apple, LinkedIn and Yahoo subsidiaries lost operational control since 2018, and leaves open whether more Chinese subsidiaries in Europe will be seized.
Dr. Jon KrohnHow Toyota and Total Wine Use Mathematical Optimization to Save Millions (with Jerry Yurchisin)
- A carmaker put an LLM front end on its manufacturing optimizer. Yurchisin relays a talk at Gurobi's Decision Intelligence Summit in Las Vegas: Toyota's optimization model plans vehicle manufacturing from regional demand forecasts, and planners query it in natural language with what-if scenarios, such as tariffs swinging from 0% to 10% to 80% or a supply being halved, without writing models or Python. He calls this a new way to give optimization to the domain experts who make the decisions.
- A retailer's data scientists learned optimization without operations-research training. Yurchisin says Total Wine faced a massive purchasing problem (pack sizes, quantities and delivery timing for every beer and wine) where machine learning "was not cutting it", and a data-science team with no operations-research training built optimization in-house with help from Gurobi's support staff. He says they now save "a lot of money" but declines to give a number, and adds that many such customers would rather not discuss it, calling optimization "the best kept secret in decision-making".
Dr. Jon KrohnLearn Mathematical Optimization for Free (with Gurobi’s Jerry Yurchisin)
- Learning optimization requires no enterprise licence. Yurchisin says his role at Gurobi, for almost four years, has been bringing optimization to the data science and AI community.
pip install gurobipygives a free, size-limited licence that is enough to model a smaller version of a real business problem, though not ones with millions or billions of decisions. - An optimization model is a "mathematical twin" of the business. Yurchisin's framing is that you represent your problem or system in math, code it in Python and call optimize to get the optimal answer, so a small-scale version can still accurately reflect the real problem.
- Free courses pair Gurobi's material with outside practitioners. He lists gurobi.com/learn and a four-part Udemy course built with a Georgia Tech analytics master's professor. There are also three YouTube playlists in an "Optimization for Data Scientists" series starting at Opti 101, with a 301 teaser due at the end of November, featuring a consultant from the firm Decision Spot who brings real client problems.
GeoHussarThe worlds major #CentralBanks are forced to buy #US dollars when we overprint them
- Foreign dollar buying acts as a fail-safe under the dollar. He argues other countries buy US dollars with their own currency to keep their trade competitive, which cheapens their currencies, strengthens the dollar, and so automatically stops it from going into "too much of a free fall."
GeoHussar#Europe, #China #Japan start printing more of their own #money when the US goes on a printing spree
- US money printing forces rivals to print their own currency. He argues that when American products become cheaper, Europe, China and Japan print more money to keep their exports competitive against the US. He says the People's Bank of China, the Bank of Japan and the European Central Bank issue that currency by buying billions of US dollars and trading them for their own.
GeoHussar#China distribution of their #Currency the #YUAN has skyrocketed over the past 25 years. #Economy
- China's larger money supply is not explained by population alone. He says China, the EU and the US hold about 90 trillion of the roughly 140 trillion in global money supply. He grants population is part of China's lead, but notes that in the early 2000s, with over a billion people against roughly 250 million Americans, China's money supply was slightly smaller than or equal to the US's; the clip cuts off before he completes the point.