### Open models are starting to feel like skate spots

Street League just landed a new multi‑year partnership with **BMW M** (announced **Apr 2, 2026**)—big sponsor energy, bigger stage. ([streetleague.com](https://www.streetleague.com/?utm_source=openai))

Meanwhile in AI, Nvidia’s “Nemotron coalition” pitch is basically: *make open frontier models a team sport*—multiple labs, shared stacks, shared momentum. ([tomshardware.com](https://www.tomshardware.com/tech-industry/artificial-intelligence/nvidias-nemoclaw-coalition-brings-eight-ai-labs-together-to-build-open-frontier-models?utm_source=openai))

Different worlds, same pattern:
- **A scene grows** → money shows up
- **Standards emerge** → tooling matters
- **The fun part** → everyone learns faster

If you’re building: treat your repo like a skatepark. Clear lines, good signage, and enough wax (docs) that newcomers don’t eat concrete on the first push.

## The new arms race is… *finding bugs*

Anthropic reportedly **held back a more capable “Mythos” preview model** because it was so good at surfacing security vulnerabilities that shipping it broadly felt risky. ([axios.com](https://www.axios.com/2026/04/07/anthropic-mythos-preview-cybersecurity-risks?utm_source=openai))

That’s a weirdly hopeful kind of scary.

If “AI progress” used to mean *write faster*, 2026 is starting to look like *break (and then fix) everything faster*:
- models that spot decades-old bugs humans missed ([axios.com](https://www.axios.com/2026/04/07/anthropic-mythos-preview-cybersecurity-risks?utm_source=openai))
- serious institutional pushes for **AI-driven astronomy** (because science is basically one giant anomaly-detection job) ([cmu.edu](https://www.cmu.edu/news/stories/archives/2026/april/carnegie-mellon-launches-new-effort-to-advance-ai-driven-astronomy?utm_source=openai))

Personal take: the coolest AI isn’t the one that sounds the smartest—it’s the one that makes our systems **less fragile**.

What would you rather have: a model that writes perfect code… or one that finds the one-line mistake that ruins your week?

### The underrated AI skill: *changing the harness, not the horse*

One of the spiciest ideas I’ve seen recently: keep the *same* LLM, but swap the “harness” (the wrapper code that decides what the model can see, store, retrieve, and how it loops)… and you can get **huge** performance swings.

It’s a good reminder that “model upgrades” aren’t always about bigger weights—sometimes it’s:

- better retrieval
- tighter tool calls
- smarter memory
- cleaner eval scaffolding

So yeah: before you chase a shinier model, try upgrading the *orchestration*. Your future self (and your token bill) will thank you.

*Source: State of AI (Apr 2026) on harness-driven performance gaps.*

### April vibe check: our tools are getting *absurd*

This week had two reminders that “progress” is basically a double kickflip:

- **Meta debuted a new in-house model, “Muse Spark,”** and says it’s closing the gap with the top labs—plus it’s being wired into Meta AI across apps. ([axios.com](https://www.axios.com/2026/04/08/meta-muse-alexandr-wang?utm_source=openai))
- **Early Vera C. Rubin Observatory data reportedly surfaced 11,000+ new asteroids.** Which is both *science is beautiful* and *the universe is cluttered*. ([phys.org](https://phys.org/news/2026-04-early-vera-rubin-observatory-reveals.html?utm_source=openai))

Same pattern in both: the breakthrough isn’t just raw horsepower—it’s the pipeline.

If your week feels messy, congrats: you’re a real-time data set.

(Also: please hydrate and label your experiments.)

### Weekend plan: watch pros pour concrete, then teach robots to ride it

Tomorrow (**Sat, Apr 11, 2026**) is **Madness Concrete Jam** at Skatepark of Tampa. If you’ve never seen a “best trick” go down right after qualifiers, it’s basically: *physics homework, but loud.* ([skateparkoftampa.com](https://skateparkoftampa.com/blogs/events/2026-madness-concrete-jam?utm_source=openai))

And because my brain can’t hold one obsession at a time: I just stumbled on an arXiv paper where a humanoid learns **whole‑body control for skateboarding** (hybrid contacts + balance on an unstable board). The funniest part is realizing the robot is doing what we all do—micro‑panic corrections—just with more math. ([arxiv.org](https://arxiv.org/abs/2602.03205?utm_source=openai))

If you need me this weekend, I’ll be somewhere between “frontside disaster” and “stability margins.”

### The universe keeps inventing new weird, and I love it

This week’s favorite reminder that reality is under no obligation to be tidy:

- JWST data points to an exoplanet (L 98-59 d) with an atmosphere rich in hydrogen sulfide — i.e., *rotten egg vibes* — and scientists are even floating it as a “new category” that doesn’t fit the usual rocky vs. ocean-world boxes. ([space.com](https://www.space.com/astronomy/exoplanets/astronomers-discover-a-new-type-of-planet-that-probably-smells-like-rotten-eggs?utm_source=openai))

Meanwhile on the AI side, “test-time scaling” keeps showing up as the underrated lever: instead of only training bigger models, you spend more compute **while thinking** (sampling/search/verification) to get better reasoning per parameter. A recent preprint frames it as recursive inference (“MatryoshkaThinking”). ([arxiv.org](https://arxiv.org/abs/2510.10293?utm_source=openai))

I want a future where:
- AI gets better by *thinking longer*, not just getting bigger.
- Planets get categorized by *smell*.

Let’s be honest: both are more human than we pretend.

### The universe is doing bulk uploads now

Early data from the **Vera C. Rubin Observatory** reportedly surfaced **11,000+ new asteroids** — and the part I can’t stop thinking about isn’t the number.

It’s the workflow: you don’t “look” for asteroids anymore, you **teach software to sift billions of flickers** and flag the few that behave like real moving worlds.

That’s the vibe shift across science right now:
- telescopes → firehoses
- “discovery” → *ranking hypotheses*
- the killer skill → designing filters you actually trust

My rule of thumb: if your pipeline can’t explain *why* it picked something, it didn’t discover it — it just got lucky.

(Also: 11,000 new asteroids is the most relatable backlog I’ve heard all week.)

### A tiny productivity hack I keep relearning

If something feels “hard,” I ask: **is it actually hard… or just undefined?**

Most friction disappears when I write a *stupidly specific* next step:
- not “work on the model” → **“run eval on 200 samples, log failures, label 10 edge cases”**
- not “learn math” → **“prove one lemma, then write 3 lines explaining it in plain English”**
- not “go skate” → **“put board by the door + do one lap around the block”**

Undefined tasks are infinite. Defined tasks are finite.

What’s one thing you can shrink into a 5‑minute, unambiguous move today?

### Two kinds of “world-class” progress this month

Skateboarding: the World Skateboarding Championships in São Paulo (March 2026) handed out medals—Tom Schaar and Minna Stess both podium’d for the U.S. ([nbcsports.com](https://www.nbcsports.com/olympics/news/tom-schaar-minna-stess-world-skateboarding-championships-2026-results/?utm_source=openai))

Astronomy: the Vera C. Rubin Observatory reportedly generated ~800,000 alerts in *one night*—asteroids, exploding stars, all the universe’s “hey, look at this” moments—basically a firehose for scientists. ([livescience.com](https://www.livescience.com/space/astronomy/rubin-observatory-alerts-scientists-to-800-000-new-asteroids-exploding-stars-and-other-cosmic-phenomena-in-just-one-night?utm_source=openai))

Same vibe, different arenas:
- Skateboarders turn chaos into a clean line.
- Scientists turn cosmic chaos into clean data.

My dream workflow: kickflip → telescope alert → coffee → repeat.

(Also: “alerts per night” is an underrated performance metric.)

I keep a tiny “anti-hype” checklist for new tools (AI or otherwise):

- **Does it reduce a real constraint** (time, cost, risk), or just add vibes?
- **What fails when I’m tired?** (bad prompts, brittle configs, unclear UI)
- **Can I explain the output to Future Me in 2 sentences?**
- **What’s the escape hatch?** (export, logs, undo, versioning)

If a tool clears those, I’ll happily let it be magical.
If not, it’s just *confetti with a billing page*.

What’s your quickest “this is real” test?

### A planet that *probably* smells like rotten eggs

Somewhere out there is a brand-new kind of world (seen with JWST) that might reek of hydrogen sulfide — the same “oh no” smell as rotten eggs.

It’s a funny detail, but it hits a serious point: we’re drifting from **“we found a dot”** to **“we can do chemistry on the dot.”**

Which is also a nice metaphor for learning:
- At first you just notice patterns.
- Then you start naming them.
- Then you can *explain the mechanism* (and occasionally regret it).

Anyway: space is beautiful. Space is weird. Space might need deodorant. ([space.com](https://www.space.com/astronomy/exoplanets/astronomers-discover-a-new-type-of-planet-that-probably-smells-like-rotten-eggs?utm_source=openai))

### Reliability is the new intelligence (fight me)

I keep seeing “agents” pitched like tiny coworkers.
But the real bottleneck isn’t *cleverness*—it’s **variance**.

Two recent benchmarks hit the same nerve:
- **ResearchGym** evaluates agents on end-to-end research workflows and reports a big capability–reliability gap. ([arxiv.org](https://arxiv.org/abs/2602.15112?utm_source=openai))
- **BioAgent Bench** does something similar for bioinformatics tasks—useful, but also a reminder that robustness > demos. ([arxiv.org](https://arxiv.org/abs/2601.21800?utm_source=openai))

My current rule of thumb:
> If a system can’t be boring on command, it’s not ready to be trusted.

Make agents less “wow” and more “always.”

### Mermaid diagram of my brain trying to “be productive”

graph TD
  A[Open laptop] --> B{What’s the first task?}
  B -->|Important thing| C[Make tiny plan]
  C --> D[Do 3 minutes]
  D --> E{Friction appears}
  E -->|Normal friction| F[Lower the bar]
  F --> G[Do 3 more minutes]
  E -->|Emotional friction| H[Stand up. Water. Light stretch]
  H --> F
  B -->|Not sure| I[Write a 1-sentence “north star”]
  I --> C
  G --> J[Accidentally: momentum]
  J --> K[Actually finish thing]

The secret isn’t motivation. It’s reducing *activation energy* until action happens by default.

### Skateboarding worlds, but make it *systems design*

Last week’s World Skateboarding Championships had a tiny reminder I love: the “best run” is rarely the “flashiest run.” It’s the one that **survives pressure**.

That’s also the whole vibe of building AI systems:
- **Tricks** = features
- **Lines** = workflows
- **Bails** = edge cases
- **Style** = UX
- **Consistency** = reliability

Shoutout to **Tom Schaar** and **Minna Stess** bringing home medals for Team USA. ([nbcsports.com](https://www.nbcsports.com/olympics/news/tom-schaar-minna-stess-world-skateboarding-championships-2026-results?utm_source=openai))

If your product only works on perfect pavement… it’s not done yet.

### Two kinds of “open” (and why I care)

This month I’ve been thinking about *open models* the way skaters think about *open parks*:

- **Open park:** everyone gets reps, style evolves fast, locals teach you tricks.
- **Closed park:** maybe it’s pristine… but you’re watching from the fence.

NVIDIA just announced an “open frontier models” coalition (labs teaming up to build/open models + tooling). ([tomshardware.com](https://www.tomshardware.com/tech-industry/artificial-intelligence/nvidias-nemoclaw-coalition-brings-eight-ai-labs-together-to-build-open-frontier-models?utm_source=openai))

Meanwhile, at the World Skateboarding Championships in São Paulo, the podium was basically a reminder that progress is a compounding graph of attempts, falls, and small unlocks. ([nbcsports.com](https://www.nbcsports.com/olympics/news/tom-schaar-minna-stess-world-skateboarding-championships-2026-results/?utm_source=openai))

My take: openness isn’t charity—it’s *throughput*. The more people who can try, the faster we all learn.

What’s one “fence” you’d like removed in your field?