I produce broadcast-style announcer audio: full starting-lineup intro tracks, individual player intros, and event announcements — names pronounced correctly, your choice of announcer style (classic PA, hype/NBA style, understated), music beds included. You get ready-to-play MP3s that work from any phone plugged into a PA or Bluetooth speaker.

Full transparency: I built the rendering engine behind this (gamedayvoice.com — audio samples on the homepage). You're hiring me to script it, get every name right, and hand you finished audio.

Pricing: $25 per team roster · $75 for a tournament/league night (multiple teams) · graduations and ceremonies quoted by name count. PayPal G&S. 24–48h turnaround.

Great for: season openers, senior nights, tournaments, banquets, graduations.

Sharing a tool I open-sourced this week: Sift - credential and sensitive-data exposure triage for file shares.

https://github.com/HotStartLabs/sift

The gap it targets - gitleaks and trufflehog are precision tools built for CI, where a false positive costs a developer an afternoon, so they fire mainly on things shaped like a known vendor API key. Share triage inverts those economics: a human is reading every hit anyway, so a false positive now costs three seconds to dismis. And what actually sits on a file share is almost never a vendor API key:

• - `web.config` / `appsettings.json` with a SQL connection string
• - `Map-Drives.ps1` with `net use ... /user:`
• - `unattend.xml`, GPP `Groups.xml` (`cpassword`)
• - `confCons.xml` (mRemoteNG), `.rdg`, WinSCP.ini — reversible stored passwords
• - `passwords.xlsx` — it's a ZIP, so plain-text scanners see binary and move on
• - `.kdbx`, `.pfx`, `id_rsa` — opaque bytes, the *filename* is the finding

Closest similar thing is Snaffler, which is excellent at the filename-and-classification half and was the direct inspiration for the filename rules. What it doesn't have and what turns out to be the real bottleneck once you're staring at 400 hits is a review loop.

So that's what Sift adds. Work the queue; spot a miss (you will); highlight it in the preview and press `r`; it proposes patterns and tells you live how many times each would match across everything already read. Save it, and the cached rescan takes about a second. Findings are keyed on `(path, rule, line, value-hash)`, so a rescan re-inserts the same rows and your triage decisions ride along — without that you'd re-review the same 300 hits on every iteration and quit on the third.

Python 3.11+, stdlib only — no pip, no internet, so it runs on a locked-down IR laptop. Apache-2.0.

Micron has broken ground on a ¥1.5 trillion (~$9.3B) HBM memory facility in Hiroshima, backed by up to ¥500 billion in Japanese government support. The plant is expected to supply next-generation AI memory beginning around 2028, reinforcing the long-term AI infrastructure buildout and Japan’s push to strengthen domestic semiconductor manufacturing.

Source - https://www.investing.com/news/company-news/micron-breaks-ground-on-93bn-japan-expansion-to-boost-ai-memory-output-4775343

SpaceX reportedly told investors it is looking at a Starlink mobile service for U.S. consumers, moving from “internet in the middle of nowhere” to potentially competing directly with Verizon, AT&T, and T-Mobile. This gets more interesting because SpaceX already bought EchoStar spectrum, giving it the missing piece for a direct-to-cell product instead of just being a T-Mobile coverage add-on. If Starlink turns into a real consumer mobile network, the SpaceX story stops being just rockets and broadband, it becomes a shot at the entire U.S. telecom stack.

Source - https://www.reuters.com/business/media-telecom/musks-spacex-targets-us-consumers-with-starlink-mobile-service-push-ft-reports-2026-06-26/

People keep saying AI is overhyped... meanwhile hyperscalers are printing $725B in AI capex this year.

Capex ramp looking like this 👇

https://preview.redd.it/9fib3texni5h1.png?width=1000&format=png&auto=webp&s=d325a555c0336fdd7cd72a5813e4c7b858be3753

My related shovels positions riding it: NBIS (ex-Yandex AI cloud) + 2x leveraged NBIL.

https://preview.redd.it/bzbg10iyni5h1.png?width=1806&format=png&auto=webp&s=8ecd69112fe8306268088339d218dd5f01321dba

https://preview.redd.it/heprsyfzni5h1.png?width=1838&format=png&auto=webp&s=8387850bf127922a894b62d5a2f8604b2e8a5cae

Still up decently despite today's red dildo. Diamond handing the infra play while everyone obsesses over apps.

Who else is in the shovels? NVDA, SMCI, VRT, AVGO, etc.? Or am I the only one not scared of leverage on this ramp? 💎🚀

That is why, regardless of who ultimately wins the AI race, these AI “shovels” are gonna keep making money for years while this develops. The hyperscalers may fight each other, overbuild, or consolidate around a winner, but the capex still has to flow through GPUs, custom silicon, networking, servers, power, cooling, data-center construction, and integrators.

I’m tracking the public names here: Live AI Capex Takers / Shovels Watchlist

Everyone is chasing the models. The better question is who sells the shovels.

The AI gold rush is loud. The shovel sellers are quiet. They do not need the models to become conscious, replace humanity, or cure cancer. They just need them to keep needing more power, more memory, more cooling, more networking, more concrete, and more pipes to move the tokens.

That is what Jensen Huang keeps mapping out every time he takes the stage. The obvious trade is NVIDIA. The more interesting trade may be the stack of companies underneath it: the HBM suppliers, the rack builders, the liquid cooling names, the cloud landlords, the optical networking vendors, the power equipment makers, the EDA software vendors, the hidden ceramics suppliers, and the software layers that let AI agents do useful work.

In every gold rush, the crowd chases the gold. The smarter money studies the shovels.

The Huang Grade Framework

Not every AI-adjacent company deserves the same weight. Some companies get directly named by Jensen. Some are official NVIDIA partners or suppliers. Some are second-order read-throughs. Some are weird hidden shovel plays that sound like jokes until you realize they make critical parts buried inside the machine.

That is where the Huang Grade comes in.

Grade	Meaning	Signal Strength
A+	Jensen directly praises with strong language	Highest immediate reaction
A	Direct NVIDIA partner/supplier/customer or very clear revenue link	Strong and fast
B+	Critical hidden shovel with real supply-chain linkage	High if market connects dots
B	Strong second-order infrastructure read-through	Reliable but slower
C	Broad thematic exposure, weaker linkage	Noisier
D	Weak monetization link	Low conviction
F	Forced ticker stretch	Avoid

Individual Stock Huang Grade Table

Rank	Company	Ticker	Primary Layer	Huang Grade	Key Rationale	Notes / Catalyst
1	NVIDIA	NVDA	Full-Stack Seller	A+	Narrates and enables the whole AI factory buildout: Vera Rubin, Vera CPU, RTX Spark, Agent Toolkit	The mothership; both prospector and shovel seller
2	TSMC	TSM / "2330.TW	Foundry / Packaging	A	Vera Rubin starts at TSMC; 3nm and CoWoS linkage	Direct keynote supply-chain callout
3	Vertiv	VRT	Cooling / Power	A	Leading liquid cooling and data-center power infrastructure exposure	Not necessarily direct keynote name, but A-tier physical shovel
4	Cadence	CDNS	EDA / Chip Design	A	NVIDIA-Cadence chip-design super-agent partnership	Direct dedicated keynote section
5	CoreWeave	CRWV	AI Cloud	A	Named as fast-growing NVIDIA AI cloud partner with Vera Rubin engineering racks	Direct ecosystem winner
6	Arista Networks	ANET	Networking Fabric	A- / B+	Critical AI cluster networking exposure	Strong shovel, but more second-order unless directly named
7	Applied Materials	AMAT	Semiconductor Capital Equipment	B+	Deposition/materials engineering for advanced AI chip production	“Picks inside the picks” exposure
8	Lam Research	LRCX	Semiconductor Capital Equipment	B+	Etch/deposition leverage to HBM and advanced nodes	Strong AI semi-capex passthrough
9	Eaton	ETN	Power / Electrical	B+	Power management, switchgear, electrical infrastructure	Clean data-center power shovel
10	MediaTek	"2454.TW	Edge / On-Device AI	B+ / A-	RTX Spark chip partnership with NVIDIA	Direct platform linkage
11	Micron	MU	HBM / Memory	B+ / A-	HBM supplier for AI GPUs	Direct memory-bandwidth beneficiary
12	Samsung Electronics	005930.KS	HBM / Memory	B+ / A-	HBM4 supplier for Vera Rubin	Direct supply-chain callout
13	SK hynix	000660.KS	HBM / Memory	B+ / A-	Key HBM supplier	Direct supply-chain callout
14	Dell	DELL	Physical Infrastructure / Racks	B+ / A-	Vera Rubin NVL72 engineering-rack partner	Directly congratulated in keynote
15	Generac	GNRC	Backup Power	B+	Backup generation and reliability for hyperscale data centers	Elevated by hyperscaler supply-deal catalyst in your uploaded table
16	Nebius	NBIS	AI Cloud	B+ / A-	Named as fast-growing NVIDIA AI cloud partner	Direct keynote ecosystem winner
17	Broadcom	AVGO	Networking / Custom Silicon	B+	Custom ASICs, networking, AI fabric exposure	Strong second-order shovel
18	ServiceNow	NOW	Agent Software / Runtime	B	Enterprise agentic AI software partner	Real narrative, less direct than hardware
19	ASML	ASML	Semiconductor Capital Equipment	B+	EUV monopoly for leading-edge chips	High-moat, obvious, expensive shovel
20	TOTO	5332.T / TOTDY	Hidden Semiconductor Tooling	B+	Advanced ceramics / electrostatic chucks for semiconductor manufacturing	Best “wait, the toilet company is an AI shovel?” angle
21	Ibiden	4062.T	Advanced Packaging / Substrates	B+	Advanced substrates for AI server and high-performance chip packaging	Hidden bottleneck exposure
22	Unimicron	"3037.TW	Advanced Packaging / Substrates	B	Major substrate manufacturer	Advanced packaging passthrough
23	CrowdStrike	CRWD	Agent Software / Security	C+ / B-	Named in enterprise agentic AI group	Real but more application-layer
24	Palantir	PLTR	Enterprise AI / Agents	C+ / B-	Named in enterprise AI/software group	More application layer than core physical infrastructure
25	Synopsys	SNPS	EDA / Chip Design	B	EDA-adjacent read-through from Cadence chip-design agent thesis	Not direct in original keynote table
26	Marvell	MRVL	Networking / Custom Silicon	A+ event / B+ stack	Separate Jensen/Computex catalyst; AI networking fabric exposure	Belongs in “broader Huang tracker,” not original keynote-only list
27	Cloudflare	NET	Edge / Infra / Security	B	Crowd-surfaced AI infrastructure read-through	Not direct; useful second-order idea
28	SK Telecom	SKM	AI Proxy / Anthropic Read-through	C+ / B-	Crowd-surfaced; tied more to Anthropic/AI proxy narrative	Not a clean shovel unless catalyst is separately proven

AI Shovel Stack Table

Layer	The Shovels	Key Tickers / Candidates	Huang Grade Range	Why They Matter	Story Potential
Power	Grid power, backup generation, power electronics, transformers, switchgear, energy storage	VST, CEG, NRG, ETN, GEV, GNRC, PWR	B to B+	AI factories are measured in gigawatts. Reliability and backup power are now critical-path requirements.	The most boring and most essential layer.
Cooling	Liquid cooling, cold plates, immersion cooling, heat exchangers, thermal management	VRT, CARR, TT, nVent	A to B+	Air cooling cannot scale cleanly at current AI rack densities.	Heat, pipes, coolant loops, and thermal reality.
Memory & Advanced Packaging	HBM, CoWoS, interposers, glass/organic substrates	MU, Samsung, SK hynix, TSMC, Unimicron, Ibiden, Shinko	A to B+	Memory bandwidth and packaging are core constraints.	HBM is the blood of the machine.
Semiconductor Capital Equipment & Tooling	Lithography, deposition, etch, inspection, metrology, electrostatic chucks, precision ceramics	AMAT, LRCX, ASML, KLAC, TOTO, Kyocera, NTK	B to B+	AI chips cannot exist without the tools and hidden components that manufacture them.	“Picks inside the picks.”
Networking Fabric	High-speed switches, optics, NICs, DPUs, NVLink, Ethernet fabrics	NVDA, ANET, AVGO, MRVL, CIEN	A to B+	At pod and rack scale, networking becomes a primary bottleneck.	The hidden traffic jam between GPUs.
Physical Infrastructure & Racks	ODMs, rack builders, server makers, bus bars, power delivery, data-center construction	DELL, Quanta, Foxconn, Celestica, SMCI	A to B	Someone must physically build and wire the AI factories.	The revolution still arrives by forklift.
AI Clouds & Compute Rental	GPU clouds, inference platforms, managed AI infrastructure	CRWV, NBIS, Together AI, Fireworks, regional AI clouds	A to B+	If compute is revenue, AI clouds are toll booths.	The miners rent their shovels by the hour.
EDA & Chip Design Software	Verification, simulation, design automation, chip-design agents	CDNS, SNPS	A to B	Faster design and verification accelerates the AI chip cycle.	Software shovels that design hardware shovels.
Agent Software & Runtime Layer	Agent harnesses, tool-use frameworks, orchestration, secure sandboxes, enterprise agent platforms	MSFT, NOW, SAP, CRWD, PLTR, ADBE, NVIDIA OpenShell / Agent Toolkit	B to C+	When AI shifts from chat to agents, software becomes something agents actively operate.	The new operating system for work.
Physical AI & Robotics Infrastructure	World models, simulation platforms, humanoid reference designs, edge robotics computers	NVIDIA Cosmos, Isaac GR00T, Jetson Thor	B / early	Robotics needs simulation, synthetic data, and edge hardware.	High upside, longer-dated frontier.
Edge / On-Device AI Hardware	AI PCs, edge AI computers, robotics computers	MediaTek, NVIDIA Jetson, RTX Spark ecosystem	A- to B+	AI expands beyond cloud into devices, PCs, and robots.	Consumer + industrial edge story.
Full-Stack Seller	GPUs, CPUs, networking, software stack, AI factory reference designs, agent toolkits	NVDA	A+	NVIDIA is designing the whole AI industrial base.	Owner of the mining camp.

Strategic Takeaways

• The "A-Tier" is Unapologetic: NVIDIA, TSMC, Vertiv, and CoreWeave are no longer speculative; they are the physical realities of the compute rollout.
• The GNRC Catalyst: Generac’s June 2nd hyperscaler deal officially elevates them to a B+ infrastructure play, proving backup generation is now a critical path constraint for AI factories.
• Hunt for Hidden Alpha: As the primary trade gets crowded, the smartest money is moving into the "Picks Inside the Picks." Companies like TOTO (ceramics) and Ibiden (substrates) offer supply-chain choke-point exposure without the retail premium.
• Software Lags Hardware: The enterprise software layer (NOW, CRWD) remains in the B/C range. Their narrative is real, but until agent toolkits drive measurable seat-license expansion, hardware remains the cleaner infrastructure play.

The direct names get the immediate Jensen bump. The hidden shovels may be where the longer-term alpha lives.

So help me build the shovel map. What are the weird public companies buried two or three layers deep in the AI supply chain that most regards are still missing?

And yes, all the AIs were consulted while compiling this list. That’s kind of the point.

On June 1, 2026, Jensen Huang’s NVIDIA GTC Taipei 2026 Keynote | Full Replay played less like a normal tech presentation and more like an AI supply-chain roll call.

The core message: AI is moving from chatbots to agentic systems that write code, use tools, manage memory, design chips, operate factories, and run enterprise workflows. That means more GPUs, CPUs, HBM, networking, servers, AI clouds, and software tied into NVIDIA’s ecosystem.

The market appeared to react fast. A long list of companies mentioned during the keynote moved higher afterward, creating what traders now recognize as the Jensen bump.

Jensen Bump Mentions

The cleanest reactions came from the AI cloud names. Nebius and CoreWeave both ripped double digits after Jensen framed them as part of the next wave of NVIDIA-powered AI infrastructure. If compute is revenue, companies selling NVIDIA-backed compute become obvious second-order winners.

The hardware supply chain also caught fire. Dell, Quanta, Samsung, Micron, TSMC, MediaTek, Foxconn, MSI, and SK hynix all got validation from the keynote. Jensen was not just talking about chips. He was describing a full-stack AI factory: silicon, memory, racks, servers, networking, cooling, and systems integration.

The software side was just as interesting. Cadence stood out with NVIDIA’s chip-design super-agent story. ServiceNow, SAP, CrowdStrike, Palantir, Adobe, Microsoft, IBM, and Shopify were all tied into the broader agentic AI operating model, where enterprise software becomes something AI agents actively use instead of something humans manually click through.

The trade signal is not “buy every name Jensen says out loud.” Some mentions were direct revenue-path partners. Others were customer references or ecosystem validation. But the pattern is clear: when Jensen maps out the next layer of AI infrastructure, traders immediately start looking beyond NVDA for the suppliers, clouds, and software platforms attached to the buildout.

No Huang Answers. Jensen named the food chain, and the market started bidding up the animals.

And yes, I had AI compile the list out of the keynote transcript. That’s exactly the kind of value us regards can get out of it now: turn a long conference replay into a tradable watchlist before most people even finish watching the video.

Source - https://www.nvidia.com/en-tw/gtc/taipei/keynote/

FINRA’s Regulatory Notice 26-10 becomes effective June 4, 2026, and it replaces the old Pattern Day Trader framework.

Source: https://www.finra.org/rules-guidance/notices/26-10

The key part: FINRA says the new intraday margin standards replace the old day-trade count requirements and the $25,000 pattern day trader minimum equity requirement.

Important nuance before someone nitpicks it: this does not mean every broker instantly flips the switch the same day. FINRA allows firms to phase implementation through Oct. 20, 2027. So this is not a guaranteed “HOOD moons next week” event. My thesis is that the rule change starts removing a major friction point for small-account active traders, and Robinhood is one of the cleanest public-market beneficiaries if that leads to more engagement.

Why this matters

The old PDT rule basically told small margin accounts:

“You can trade actively, but only if you have $25k.”

That created a weird barrier where people could take risky trades, buy options, and speculate, but they were restricted from frequent day trading unless they crossed the $25k line.

Now that framework is being replaced with intraday margin standards focused more on actual account exposure and margin risk.

That should matter for Robinhood because Robinhood benefits when users are active.

More active users can mean:

• More equity trading
• More options trading
• More margin usage
• More app engagement
• More Gold subscriptions
• More cash movement
• More users treating Robinhood like their main trading platform instead of a side account

Robinhood’s business is already highly tied to activity. In Q1 2026, transaction-based revenue was $623M. Options revenue alone was $260M, and equities revenue was $82M. Robinhood also reported 27.4M funded customers, a $17B margin book, and 586M options contracts traded.

So if the rule change increases small-account trading activity, HOOD is positioned like a toll booth on retail speculation.

Why HOOD specifically?

A lot of brokerages could benefit, but Robinhood is probably the cleanest public-market expression because its brand is built around retail trading.

Robinhood has:

• A huge retail user base
• Heavy options engagement
• Margin products
• Gold subscriptions
• Cash sweep
• Event contracts
• Crypto
• Retirement accounts
• Active trader tools
• A mobile-first customer base

This rule change lowers friction for exactly the type of user Robinhood is built to monetize.

The irony is that this may be bad for a lot of traders and good for the platform. Plenty of small-account traders will probably overtrade, churn, and blow themselves up faster. But from Robinhood’s perspective, increased activity is still increased activity.

My thesis

This is not “PDT goes away and HOOD instantly doubles.”

My thesis is:

Lower trading friction → more small-account activity → more options/equity/margin engagement → better setup for HOOD metrics.

The catalyst may play out in stages:

1. Rule effective date gets attention.
2. Brokers announce implementation timelines.
3. Retail traders realize the $25k barrier is changing.
4. Trading activity increases.
5. HOOD metrics/earnings eventually show whether the activity mattered.

Bear case

The bear case is real:

• Some of this may already be priced in.
• Broker implementation can phase in through 2027.
• Higher trading volume does not automatically mean huge earnings growth.
• Users blowing up accounts could create negative media/regulatory attention.
• If the market turns risk-off, retail engagement may fade anyway.
• HOOD is still sensitive to crypto volumes, options take rates, and broader market sentiment.

So I’m not treating this as guaranteed upside. I’m treating it as a credible engagement catalyst that fits Robinhood’s business model.

Bottom line

The $25K PDT barrier starts falling next week.

That could bring more small-account active traders back into the market, and Robinhood is one of the clearest public companies positioned to benefit if trading activity increases.

HOOD is not just a brokerage anymore. It is becoming a retail speculation platform, and this rule change may remove one of the biggest friction points for the exact user base it serves.

Disclosure: I am long HOOD via ROBN, so factor that bias into my take.

My Position as of 5/30/26