Blake Yoder

Building Something New™

[Blake Yoder @blakeyoder]

@HealthcareAIGuy? @nikillinit?

[Blake Yoder @blakeyoder]

Is there anyone building a platform that lets me build a SMART-on-FHIR app once and then distribute it into many EHRs (Epic, Oracle Health/Cerner, Athena, etc.) without separately negotiating and integrating with each health system? I’d pay for this, today.

[Blake Yoder @blakeyoder]

@RaminNasibov

[Blake Yoder @blakeyoder]

@kieranklaassen I hear you. I find a tracker like Linear to be helpful for canonizing product decisions and conversations with the agents. With access to the cli, it’s a good documentation layer for agents and humans. I rarely look at it, but when I do it’s quite useful to see the breadcrumbs

[Blake Yoder @blakeyoder]

@nikillinit 100% - still can’t believe my first job out of college was from showing up to my university’s job fair. Feels like the Jurassic era at this point. Very thankful they took a chance on a “kid”.

[Blake Yoder @blakeyoder]

@willlenzen @wimpdecaf @whale Hi @willlenzen and @whale - great folk in this thread!

[Blake Yoder @blakeyoder]

@tjparker “We Shall See” is what my teacher would always say. Lovely parable of wisdom.

[Blake Yoder @blakeyoder]

@kapilansh_twt https://t.co/1nDBkj4tWo - only need one private key to share. all env vars are encrypted into ciphertext and you can just straight yeet up to github. wish I had this years ago...

[Blake Yoder @blakeyoder]

@michael_chomsky Yeah, I'll DM you. Got a good one for finding specialists and getting booked quickly.

[Blake Yoder @blakeyoder]

@realdocspeaks Sent!

[Blake Yoder @blakeyoder]

@arseniycodes Yes, it’s killer at building decks and materials from our sites design theme

[Blake Yoder @blakeyoder]

@claudeai Did anyone just absolutely rip through their usage today? Doing the same work today as yesterday and absolutely tore through my session usage and $50 in extra-usage...

[Blake Yoder @blakeyoder]

If you’re building in healthcare, do the due diligence at least of not including Dr. Sarah Chen as one of your “doctor testimonials”….

[Blake Yoder @blakeyoder]

@andruyeung @brian_armstrong 2 is hard to buy right now. How does AI let managers double/triple their reports? At that point, just flatten it to the extreme and have no managers - check out Valve’s employee handbook, specifically the section “Welcome to Flatland”

[Blake Yoder @blakeyoder]

@Samaytwt Alright, I’ll bite. Where are the 9-5 high salary jobs? Height of pandemic, sure. Now? A dying breed.

[Blake Yoder @blakeyoder]

I got this email today. Kinda wild how antiquated this reads…

[Blake Yoder @blakeyoder]

bruh, thought opus 4.7 was nerfmogging me for like two days and realized I was ripping and shipping with...sonnet 4.6 🤡

[Tyler @rezoundous]

Opus 4.7 is insane guys. It one shotted my session usage limit.

[Varun @varun_mathur]

Introducing Pods

Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL.
A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management.

There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own.

Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live.

What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data.
- No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on.
- Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online.
- Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it.
- Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using.

Coming soon:
- Pod federation: pods form alliances with other pods.
- Marketplace: pods with spare capacity can sell inference to other pods.