DeepReach AI

Data Refinery for Physical AI. DM for data collaboration or model fine-tuning.

[DeepReach AI @DeepReach_AI]

We’ve reached $1M ARR within 3 months of launching our platform.

[Chris Liu @drxcliu]

@DeepReach_AI and RealHand AI hosted a robotics panel in Palo Alto. Super excited to have researchers and executives from @nvidia/@Meta/@xai/@GoogleDeepMind/@Waymo.

[Tim Li @TimLi_DR]

ABB + NVIDIA Omniverse is an important signal.
But it also highlights a bigger problem:
Robotics doesn’t have a hardware problem.
It has a deployment problem.
Most automation is built for large factories.
The real opportunity is SMB manufacturing, where robots must be:
lightweight
affordable
fast to deploy
Physical AI will scale through data and deployment engines, not just better robots.
#Robotics #PhysicalAI #EmbodiedAI #Automation #DeepTech

[DeepReach AI @DeepReach_AI]

The very first step of VLA data collection

[DeepReach AI @DeepReach_AI]

Deploying to our first customer! A huge warehouse in Los Angeles. #Robotics #labor

[DeepReach AI @DeepReach_AI]

@oyhsu Hah that’s exactly what we help

[DeepReach AI @DeepReach_AI]

#unitree #CES26 team came to our event at demonstrating G1’s amazing moves. #robot

[DeepReach AI @DeepReach_AI]

Our founder @drxcliu shares the bottleneck of robot deployment and our vision to help robot companies run task reliably at client sites nationwide.

[DeepReach AI @DeepReach_AI]

@cb_doge that’s ambitious.

[DeepReach AI @DeepReach_AI]

@xai wow

[DeepReach AI @DeepReach_AI]

Our first hoodie is out!

[DeepReach AI @DeepReach_AI]

@0xmitsurii He was already a billionaire that time with IPO. But still respect

[DeepReach AI @DeepReach_AI]

@elonmusk @elonmusk check the necklace 👀 clear proof of OAI trying to distill from @Gemini or stealing it’s data

[Millie Marconi @MillieMarconnni]

Stanford just pulled off something wild 🤯

They made models smarter without touching a single weight.

The paper’s called Agentic Context Engineering (ACE), and it flips the whole fine-tuning playbook.

Instead of retraining, the model rewrites itself.

It runs a feedback loop write, reflect, edit until its own prompt becomes a living system.

Think of it as giving the LLM memory, but without changing the model.
Just evolving the context.

Results are stupid good:

+10.6% better than GPT-4 agents on AppWorld
+8.6% on finance reasoning
86.9% lower cost and latency

The trick?
Everyone’s been obsessed with clean, minimal prompts.
ACE shows the opposite: long, dense, self-growing prompts win.

Fine-tuning was about changing the model.
ACE is about teaching it to change *itself.*

This isn’t prompt engineering anymore.
It’s prompt evolution.

[DeepReach AI @DeepReach_AI]

@StartupArchive_ @TimLi_DR

[DeepReach AI @DeepReach_AI]

Next-gen GEPA. Proved the power of reasoning improvement just with context https://t.co/guXaXW2HMk

[DeepReach AI @DeepReach_AI]

@OpenAIDevs Aligned with our vision: eval is more important than ever

[DeepReach AI @DeepReach_AI]

@_devJNS Nah Gemini is on the way