AI compute isn't just for inference 2/10
Anthropic’s huge compute commitments raise a deeper point: a lot of compute may be more valuable in research, pre-training, and post-training than in inference alone.Inference monetizes today. Research can improve every GPU going forward.
The AI economy, part 1
It looks at why the build-out is no longer just about GPUs: it’s also about power, cooling, networking, construction, and financing. It also introduces a self-reinforcing loop: better AI → more value → more compute → better AI.
#AI #DataCenters
Multi-agent systems need orchestration
More agents ≠ better results.
Expertise can get diluted, communication can add noise, and long-horizon systems can lose context. The harder problem is orchestration: route the right expertise, limit unnecessary communication
Long-horizon agents need layered memory
A long-running agent also needs its own recoverable trajectory: what it tried, what changed, what failed, and what to revisit. ACM + PRO-LONG point toward layered working, episodic, structured-state, and semantic memory.
Long-Horizon Agent Memory Is a Selection Problem
Bigger context windows do not solve long-horizon agent memory. Over hours or
days, the key challenge is deciding what stays detailed, what gets compressed,
what should be retrieved. Memory is selection,
not just summarization.
Unsupervised On-Policy Self-Distillation
No teacher. No environment hint.
Run the model many times on the same problem. If runs disagree, the majority answer becomes the training signal.
many runs → disagreement → consensus → retrain → better policy
Part 3 of our OPSD.
On-Policy Self-Improvement
An agent calls a tool that doesn't exist.
Instead of discarding the failed trajectory, the environment supplies a targeted hint such as “Tool not found.”
trajectory → mistake → hint → training signal → improved policy
Part 2 of our OPSD series.
On-Policy Self-Improvement
Off-policy: a student learns from trajectories generated elsewhere.
On-policy: the student learns from trajectories it generates itself
But if we remove the teacher entirely, where does the improvement signal come from?
Part 1 of a 3-part series
Why open source in the AI era
AI makes software cheaper to build and easier to reproduce. That shortens the life of technical differentiation.
One response: open source — trade implementation exclusivity for distribution, feedback, reputation, relationships, and faster learning.
Open-weight models are changing the enterprise AI playbook. Proprietary data can become a fine-tuned internal asset, but training must work across different accelerators and compute platforms. That orchestration layer is what we are building at Brazen Lab.
A chatbot answers. An agent acts. The hard part is making every step reliable: observing systems, choosing tools, taking actions, and evaluating the full trajectory—not just the final answer. #AIagents
I had six AI-generated videos ready to publish and scrapped every one.
The feedback split cleanly: people liked the content, and disliked that my voice was synthetic.
So now it's unrehearsed and unpolished. Same topics: reliable agents, open-source models.
Hossein Akhlaghpour retweeted
Hiring for an unusual role @mirendil: Technical Chief of Staff, GTM & Ops. I'm looking for someone exceptional, high-energy, and technically fluent who'll work directly with me and touch everything: strategy, GTM & operations, multiplying our impact.
jobs.ashbyhq.com/mirendil/b1…
If you are passionate about accelerating science, join us!
Today, I’m excited to formally announce @mirendil with my amazing co-founders Harsh Mehta, Shayan Salehian, and Tara Rezaei!
We’re fortunate to work with @a16z and @kleinerperkins, who led our seed round of $200M, followed by a major investment from NVIDIA, among others.
Mirendil exists to accelerate science and technology, and through them, to help solve humanity's most pressing problems.
Self-accelerating AI R&D is the most direct path to delivering on AI's broader promise, which is why we believe the most important application of AI is AI itself. Get this loop right, and it compounds. It fundamentally changes the rate of progress itself across all domains.
We believe this capability should be democratized. It should be used to power all scientific efforts trying to innovate at the frontier. There are far more important problems—and broader ones—than any single lab can take on, so more groups should be able to pursue them.
This pulls concentration of power away from a few labs: businesses and science labs can own their AI and infrastructure, keep their margins, and control their own destiny instead of ceding it all to a single AI lab.
We’re a small team with a singular focus. Our founding team consists of 20 researchers and engineers from frontier institutions including Anthropic, xAI, Google DeepMind, and OpenAI, united by a passion for science and a drive to build the technologies that move it faster. If you want to build the system that builds systems, join us!
@HarshMeh1a, @shayan_, @tararezaeikh
Hossein Akhlaghpour retweeted
My name might be on the ballot right now, but that ballot line belongs to the people of Maine.
Intro to a technical AI series on enterprise AI capacity and trust from brazenlab.ai
3/ That's the series: capacity and trust, from an engineer's seat.
Real experiments, real failures, actual code — no product pitch.
New episodes several times a week, sometimes daily.
Everything → brazenlab.ai
2/ Trust.
Give an agent tools and it'll happily follow a malicious instruction it read on a web page.
The work: red-team agents the way a real attacker would, and harden them before someone else finds the hole.