Geek Coder

[Geek Coder @Muhammad_DMA]

@The_DragonLadyy If two neighbors are fighting, Lahore must have visited one

[Geek Coder @Muhammad_DMA]

@AUSSIEPRIDminc @bermudauk70 @7signxx That's not a stick, That's a sharp tool they use to cut wheat. Clear intent that he wanted to murder

[Vaibhav Sisinty @VaibhavSisinty]

Did xAI just mass-murder the entire voice AI industry? 🤯

Grok just launched two voice APIs. Speech-to-Text and Text-to-Speech.

Built on the same stack powering Tesla cars and Starlink support.

And priced at 10x cheaper than ElevenLabs.

Speech-to-Text: $0.10/hr batch. $0.20/hr streaming.

Text-to-Speech: $4.20 per million characters.

25+ languages. Real-time streaming. Speaker diarization.

Already outperforming ElevenLabs, Deepgram, and AssemblyAI on word error rate.

TTS ships with expressive tags like [laugh], [sigh], <whisper>, <emphasis>.

Voices that don't sound like robots reading a script.

ElevenLabs spent years building a voice AI company.

xAI built voice AI for cars and satellites.

[NVIDIA AI @NVIDIAAI]

Here's your weekend project. Build a fully local, sandboxed AI assistant.

Step-by-step tutorial to build your always-on agent:

🦞 on openclaw
✅ with NVIDIA NemoClaw
✨ using NVIDIA DGX Spark

Get started: https://t.co/EoXBTKpPIh

[Vaibhav Sisinty @VaibhavSisinty]

Microsoft just killed voice subscriptions. 🤯

They quietly open-sourced VibeVoice, and it’s a total industry disruptor.

Transcribe hour-long meetings or generate 90 minutes of natural, multi-speaker speech, all locally on your hardware for free.

It handles 50+ languages and complex speaker tracking without the monthly fees.

The era of expensive voice apps is officially over.

I share updates like these in my free AI community on WhatsApp.

Join here

https://t.co/In1GODM4A9

[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.

[South Asia Index @SouthAsiaIndex]

“We are Israel's real brothers. We also support what they have been doing in Lebanon. I hope they drop 100 more bombs in Lebanon and drop 50 more bombs on Gaza.”

◾ Indian Journalist on LIVE show

[Geek Coder @Muhammad_DMA]

@NeverteIImeodd is that man even the father?

[Geek Coder @Muhammad_DMA]

@DeRonin_ lol This has to be rage bait 🤣