Bittensor subnet news

Join us on the next episode of Novelty Search to find out how you can spend less running your AI agents. @oli_soma will show you how to save money with SOMA’s context compression. You’ll also hear from @Hedgehog_ai about @DendriteHQ’s story and what’s next. Thursday :: 5PM EDT https://t.co/iehpdLXhkR

100% agree ☀️ https://t.co/rdaXojNDbr

@say_gm_ Plug SOMA into your agent: https://t.co/CF6bySe17P

SOMA × GM is live. We compress context and route agent requests to @say_gm_. They provide the model inference. Two Bittensor subnets working together, with real traffic flowing through the integration. Compress. Pay less. https://t.co/rfMqxsGPw1

Fruit Fly learned how to pay less for AI agents. You can learn it too. Plug in SOMA today: https://t.co/CF6bySeyXn Compress. Pay less. https://t.co/uI4ZMGcfNv

Plug SOMA into your pipeline: https://t.co/etBanUYNgi

39.7% cheaper input with SOMA. We tested our context compressor against GPT-6 Astra. The original prompt used approximately 12,937 tokens, with an estimated input cost of $0.1294. We then took the exact same prompt, compressed it with SOMARIZER at a 0.60 compression ratio, and https://t.co/fEUmLbzM0B

@japanese_crispy @oli_soma Try it here: https://t.co/CF6bySe17P

SOMA (SN114) Is Bringing Bittensor Into GitHub Copilot… Here’s Why That’s A Big Deal

We all know that most Bittensor products live inside Bittensor. Dashboards, Discord servers, subnet leaderboards, token charts. So it only… Continue reading on Medium »

SOMA Highlights - September 9 We opened SOMA to everyone. One competition later, savings went from 10% to up to 15%. Try it in GitHub Copilot with DeepSeek V4 Pro. This time @japanese_crispy joins @oli_soma. Watch this week’s update below. Compress. Pay less. https://t.co/fHM0ZX8O1e

We taught SOMA a new trick. The new compression algorithm improved on the previous one by 5pp, so you can now save up to 15%. Put it to work: https://t.co/CF6bySeyXn Compress. Pay less. https://t.co/6wXbSYKaQi

Plug it into your pipeline: https://t.co/fMqSwAO455

SOMA sits between the agent and the model. Prompt → Agent → SOMA → LLM It compresses the task list before it reaches the LLM, so the model reads fewer input tokens and still solves the same task. Compress. Pay less. https://t.co/00qqF43UZF

Tokens are units of time. For AI agents, context compression turns those tokens into more working time. More work from the same compute. https://t.co/aCDqMALaP1

SOMA is now open. Plug it into GitHub Copilot and run DeepSeek V4 Pro with context compression. Try it on a real coding workload. Tell us what works, what breaks, and what we should improve. More models coming soon. Try SOMA: https://t.co/etBanUYNgi https://t.co/I4Qfxj3Ks2

Plug in SOMA: https://t.co/etBanUYfqK

SOMA is live for GitHub Copilot. All Early Access emails have now been sent. If you signed up, check your inbox. You can plug SOMA into Copilot and use DeepSeek V4 Pro with 10% savings. Every Early Access user gets $5 in credits to test it on real coding tasks. There are no https://t.co/2kGFByKmDO

Join the Early Access list now: https://t.co/F1S7McihVL

SOMA Highlights - August 30 ➡️ SOMA Early Access product coming soon ➡️ @say_gm_ integrated the SOMA algorithm ➡️ Introducing the concept of hybrid competition Full breakdown below 👇 https://t.co/fmj4vG3i5h

We’re bringing SOMA to GitHub Copilot on August 31. ➡️ Around 10% token savings ➡️ Payments in fiat or $TAO ➡️ $5 in credits for early users Over time, we’ll expand the product with more models, more harnesses, and greater savings. Sign up now: https://t.co/F1S7McihVL https://t.co/9gEiyHxHOD

The best product feedback is a production deployment you didn’t ask for. @say_gm_ ported SOMA’s OpenClaw compression core to Rust and is running it inside their privacy-first gateway for dozens of AI models. That’s what we built SOMA for. https://t.co/gMS8eJ9LVF

This is the SOMA team - we're definitely real. Engineers and researchers who have spent years on variuos projects, distributed systems and incentive designs, backed by @DendriteHQ. @oli_soma - CEO of SOMA Matt - Owns SOMA's technology end to end and leads the engineering org: https://t.co/NoIUxzN5Tf

Agentic workloads are turning context into one of the largest components of inference spend. As agents run longer loops and make more calls, the same context gets processed again and again. Join to SOMA Early Access here: https://t.co/F1S7McihVL https://t.co/1VgBmirsXX

Join the Early Access list now: https://t.co/F1S7McihVL

Agentic inference changes the economics of tokens. A chat request consumes context once. An agent can reuse and expand the same state across dozens or hundreds of inference steps - which makes context size, KV-cache pressure and memory bandwidth increasingly important https://t.co/b8RzIUkUYu

Join the Early Access list now: https://t.co/F1S7McihVL

SOMA Highlights - August 21 ➡️ Early access sign-ups are open ➡️ @DendriteHQ team is testing the SOMA compressor ➡️ 10% token savings achieved this round - 20% is the next target ➡️ Scout, our coding task generation pipeline, is in final testing ➡️ Compression models are now https://t.co/HuhCkMMcvC

Live now: @oli_soma with @gordonfrayne talking SOMA - context compression and more insights into what we're building on SN114. https://t.co/LfeyEq1vkq

Everyone building on language models pays per token. Few can explain what a token is, how it gets counted, or why input tokens dominate agent workloads. We wrote it out from the bottom up, tokenizer to invoice: https://t.co/jhUbitDyY1

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