
Handoris Herrongtin
181 posts



Inference Chips for Agent Workflows @sdianahu Most AI chips are designed for "prompt in, response out." Agents don't work that way. They loop, branch, and hold context across dozens of steps, and current GPUs hit 30–40% utilization as a result. That gap is where purpose-built silicon wins.



For GBrain I built a proper eval harness. 145 queries, Opus-generated corpus. The retrieval stack uses graph based, vector based and Grep based strategies in combination. The graph layer is worth +31 points on precision. Vector-only misses 170/261 correct answers that the full system finds. Keyword + vector + graph are three separable wins, each load-bearing. Standard information retrieval metrics: the same ones Google uses to measure search quality. Precision at 5: You ask a question, the system returns 5 results. How many of those 5 are actually useful? If 3 out of 5 are relevant, P@5 = 60%. It measures: am I wasting your time with junk results? Recall at 5: For a given question, there might be 3 pages in the entire brain that are genuinely relevant. If the system finds all 3 in its top 5, R@5 = 100%. If it only finds 1, R@5 = 33%. It measures: am I missing things you need? High precision = low noise. High recall = nothing slips through. GBrain's 97.9% R@5 means it almost never misses the right answer. The 49.1% P@5 means about half the results are relevant — which is good when you realize that for most queries there are only 1-2 right answers out of 17,888 pages, so 2.5 hits out of 5 is strong signal. Entity resolution is zero-LLM-call: regex extracts typed links (works_at, invested_in, founded) on every write. Re-embed on write not on a timer, so decay = stale pages, and stale pages get rewritten when new info lands. Scorecards: github.com/garrytan/gbrai…










My conversation with Marc Andreessen (@pmarca), co-founder of @a16z and Netscape. 0:00 Caffeine Heart Scare 0:56 Zero Introspection Mindset 3:24 Psychedelics and Founders 4:54 Motivation Beyond Happiness 7:18 Tech as Progress Engine 10:27 Founders Versus Managers 20:01 HP Intel Founder Legacy 21:32 Why Start the Firm 24:14 Venture Barbell Theory 28:57 JP Morgan Boutique Banking 30:02 Religion Split Wall Street 30:41 Barbell of Banking 31:42 Allen & Company Model 33:16 Planning the VC Firm 33:45 CAA Playbook Lessons 36:49 First Principles vs. Status Quo 39:03 Scaling Venture Capital 40:37 Private Equity and Mad Men 42:52 Valley Shifts to Full Stack 45:59 Meeting Jim Clark 48:53 Founder vs. Manager at SGI 54:20 Recruiting Dinner Story 56:58 Starting the Next Company 57:57 Nintendo Online Gamble 58:33 Building Mosaic Browser 59:45 NSFnet Commercial Ban 1:01:28 Eternal September Shift 1:03:11 Spam and Web Controversy 1:04:49 Mosaic Tech Support Flood 1:07:49 Netscape Business Model 1:09:05 Early Internet Skepticism 1:11:15 Moral Panic Pattern 1:13:08 Bicycle Face Story 1:14:48 Music Panic Examples 1:18:12 Lessons from Jim Clark 1:19:36 Clark Versus Barksdale 1:21:22 Tesla Versus Edison 1:23:00 Edison Digression Setup 1:23:13 AI Forecasting Myths 1:23:43 Edison Phonograph Lesson 1:25:11 Netscape Two Jims 1:29:11 Bottling Innovation 1:31:44 Elon Management Code 1:32:24 IBM Big Gray Cloud 1:37:12 Engineer First Truth 1:38:28 Bottlenecks and Speed 1:42:46 Milli Elon Metric 1:47:20 Starlink Side Project 1:49:10 Closing Includes paid partnerships.

Reason why he didn’t kill him, it’s still one of the biggest mystery



🚨 SAM ALTMAN: “We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter.”











Tonight, we reached an agreement with the Department of War to deploy our models in their classified network. In all of our interactions, the DoW displayed a deep respect for safety and a desire to partner to achieve the best possible outcome. AI safety and wide distribution of benefits are the core of our mission. Two of our most important safety principles are prohibitions on domestic mass surveillance and human responsibility for the use of force, including for autonomous weapon systems. The DoW agrees with these principles, reflects them in law and policy, and we put them into our agreement. We also will build technical safeguards to ensure our models behave as they should, which the DoW also wanted. We will deploy FDEs to help with our models and to ensure their safety, we will deploy on cloud networks only. We are asking the DoW to offer these same terms to all AI companies, which in our opinion we think everyone should be willing to accept. We have expressed our strong desire to see things de-escalate away from legal and governmental actions and towards reasonable agreements. We remain committed to serve all of humanity as best we can. The world is a complicated, messy, and sometimes dangerous place.



Tonight, we reached an agreement with the Department of War to deploy our models in their classified network. In all of our interactions, the DoW displayed a deep respect for safety and a desire to partner to achieve the best possible outcome. AI safety and wide distribution of benefits are the core of our mission. Two of our most important safety principles are prohibitions on domestic mass surveillance and human responsibility for the use of force, including for autonomous weapon systems. The DoW agrees with these principles, reflects them in law and policy, and we put them into our agreement. We also will build technical safeguards to ensure our models behave as they should, which the DoW also wanted. We will deploy FDEs to help with our models and to ensure their safety, we will deploy on cloud networks only. We are asking the DoW to offer these same terms to all AI companies, which in our opinion we think everyone should be willing to accept. We have expressed our strong desire to see things de-escalate away from legal and governmental actions and towards reasonable agreements. We remain committed to serve all of humanity as best we can. The world is a complicated, messy, and sometimes dangerous place.




