Lawrence D. Loeb

14.2K posts

Lawrence D. Loeb

Lawrence D. Loeb

@LDLoeb

New York, NY Katılım Mayıs 2009
1.6K Takip Edilen412 Takipçiler
Lawrence D. Loeb
Lawrence D. Loeb@LDLoeb·
@Angry_Staffer Look, Trump launched this war to initiate regime change. In November his critics will be shown that he achieved his goal. They just stupidly assumed he meant Iran!
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Lawrence D. Loeb
Lawrence D. Loeb@LDLoeb·
@jukan05 Sounds like someone from the outside creating/fanning discontent to take advantage of management’s opaque style.
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Lawrence D. Loeb
Lawrence D. Loeb@LDLoeb·
@Midnight_Captl @tszzl Professionals? That describes most people not diving in to learn what it actually is - and even some of them! I’ve seen people who claim to know about AI in one breath and saying that it’s all hype in the next because it is probabilistic and not determinative.
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Midnight Capital
Midnight Capital@Midnight_Captl·
@tszzl The hardest part for most professionals is that AI changes so fast. You finally build an opinion about what the models can or can’t do, and then a few weeks later that opinion is already outdated.
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roon
roon@tszzl·
after introducing an elite lawyer friend to 5.5 pro the models do not sell themselves and one of Claude’s great successes has been packaging them up and marketing usecases for many verticals
roon tweet mediaroon tweet media
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Lawrence D. Loeb
Lawrence D. Loeb@LDLoeb·
@Midnight_Captl @canyoudugit8 @Jespabe First, I think it’s the baseball caps. They’re really cool. Second, the AMD cult has been like this for over 30 years. If they still have the postings, you could see the same nonsense on siliconinvestor.com from 1996. If it had been true then, AMD would be 10x NVDA today.
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Midnight Capital
Midnight Capital@Midnight_Captl·
How $NVDA being down today and $AMD being up 5% today after this print makes me feel 💀
GIF
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jordy
jordy@jordymaui·
@LDLoeb i’ll return to writing weeklyclaw asap -> had a vacation and very deep in world cup work at the moment! so lacking time. my agent is running well with codex and gpt combo though, couldn’t actually tell you what version i’m on, memory okay atm!
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Lawrence D. Loeb
Lawrence D. Loeb@LDLoeb·
@jordymaui will there be another weekly claw? How are you doing with the updates? I’m still on 5.7 after a disastrous 5.5 update (12+ hours to repair). I’m also finding it losing context and becoming stupid as a result. Considering trying Hermes. What’s your experience?
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Lawrence D. Loeb
Lawrence D. Loeb@LDLoeb·
@jordymaui I’m using Codex GPT-5.5 for most things. Maybe it’s because I’m using a windows machine, but it seems they keep adding features without first stabilizing what they have. I see a lot of people saying they’re giving up because of maintenance issues and going to Hermes.
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Lawrence D. Loeb
Lawrence D. Loeb@LDLoeb·
@BenBajarin @phithetasigma So is the $20 bn stand alone Grace and stand alone Vera for fiscal ‘27? Is it Vera for fiscal ‘27 (effectively 3-6 months)? Some other combination/period?
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Ben Bajarin
Ben Bajarin@BenBajarin·
@phithetasigma He gave us this lead at GTC in our QA so a few of us could see it coming.
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sigma capitalist
sigma capitalist@phithetasigma·
On Nvidia’s CPU revenue guidance – Most, if not all, the sell-side analysts I know of have assumed the $20B CPU revenue guidance to be 100% from Vera CPU standalone sales (not including Vera CPUs in Vera Rubin systems). Can’t fault them given how this figure was framed I had to replay the Nvidia earnings call to confirm what was said about the CPU revenue number 1 - Excerpts from the prepared remarks by Nvidia CFO (Colette): Building on the success of our Grace CPU, Vera is arriving just in time to meet this inflection. […] Vera CPU opens a brand new $200 billion TAM for NVIDIA, a market we have never addressed before. And every major hyperscale and systems maker is partnering with us to get it deployed. We have visibility to nearly $20 billion in total CPU revenue this year, setting us up to become the world leading CPU supplier. 2 - Excerpts from the Q&A section: Vivek Arya (BofA Securities analyst): […] And then secondly, the $20 billion number that you gave, is that for standalone Vera CPUs, or is that kind of already included in that Vera as part of Vera Rubin? Jensen Huang: The 20 billion is for standalone CPUs. And remember, we have Vera used in 3 ways. As a standalone - 4 ways. Let me just start with the one that you already know The first way is Vera Rubin. And we will sell millions of Rubins and every two of them is connected to a Vera. And, of course, we price those too. And they are properly priced. And so that is the number one use case The second use case is Vera standalone CPU The third is Vera with CX9 and the software stack for storage. And then Vera in CX9 with the software stack for security and compute isolation and confidential computing So, each one of those use cases is built on Vera And my sense is that we will be supply constrained throughout the entire life of Vera Rubin. There are four different use cases of it. But, anyhow, the answer to your question is, the 20 billion is a standalone. Nvidia CFO then went on a phone interview with Yahoo Finance yesterday morning – during which the interviewers zoomed in on the 20 billion number: Q: When you talked about that 20 billion, is it the 20 billion as in the CPUs included with Vera Rubin, and then the standalone units as well, or is it the standalone on its own that is the 20 billion? A: it is actually both. As you have seen, already with Grace Blackwell. Grace with it, we have had standalone. And we will see it along with those full systems as well. And then again, with Vera Rubin, we are going to see more of that as well Here’s the interview link: finance.yahoo.com/news/nvidia-cf… With this new update, it is going to be more challenging than before to project the potential bit demand for LPDDR (versus when assuming the $20B to be from 100% Vera CPU sales) given that we now have not only different sales configurations for Vera, but also the sales split between Grace and Vera CPUs, both of which have different ASPs There are also two Grace CPU variants: Superchip (144 cores) and C1 (72 cores). Grace Superchip carries three LPDDR5X memory module options (240GB/480GB/960GB) whereas C1 has 120GB/240GB/480GB on-module memory options One reasonable assumption is for Vera sales to begin in Q3 this year (as part of Vera Rubin systems) followed by standalone Vera sales ramp in 2027 Given that the $20B figure for this year is quite large, one can also assume that this is anchored by revenue booked from the sales of standalone Grace CPU servers being shipped to Meta
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Midnight Capital
Midnight Capital@Midnight_Captl·
Just imagine being in the room with the Micron executives: “I don’t know Steve, 81% gross margins seems pretty high.. aren’t our customers gonna be pissed?” “Jerry. Let me frank with you. We don’t believe any of this AI shit. We need to MAX EXTRACT TODAY. Do you understand???” “Uhhh yeah I guess Steve” “Jerry, Jesus Christ, Jerry wake the hell up. This is our chance Jerry, our one and only chance out of this hell hole and onto a yacht in Majorca. The more Google calls me crying the more I just picture money stacking up to the sky. Do you see it too Jerry? The money stacking up to the sky??” “I don’t know Steve. Don’t you think this is a bit much? Can’t we just keep margins at around 70%? Make enough money to get rich still…” “Fuck that Jerry. You’re not thinking like one of them. If they swapped positions with us, they’d do the same thing to us in a heartbeat. It’s a cold cold world Jerry.” “Okay Steve, I guess.. I’m going to drop this. I’ll call Google tomorrow and tell them we’re tripling prices again…”
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Aaron
Aaron@Aaronwei3n·
@LDLoeb I do not think of it as second source as well. but Maia ? is this even being used by Microsoft?
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Lawrence D. Loeb
Lawrence D. Loeb@LDLoeb·
@Aaronwei3n I don’t think all of this TPU/XPU/etc vs GPU is about second sourcing. I think a lot of it is scarcity of compute. That’s why A100s are still in production at high prices. Of course there’s demand for Maia - and any other PU that TSMC/Samsung/INTC makes.
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Lawrence D. Loeb
Lawrence D. Loeb@LDLoeb·
@Therealtesp @Angry_Staffer The case was filed after the statutory date, so it would have been dismissed by a judge. Fortunately for Trump, the IRS (through the Treasury Department) and Justice Department report to him, so he had his employees negotiate a deal to his benefit.
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Tesp
Tesp@Therealtesp·
@Angry_Staffer I'm sure "Trump's IRS" (as if there are two different ones) is legally or civilly liable, correct? This is something that has to be challenged by a court, and settled. Was it not settled? As I understand it (I don't hang on everything) there was a civil lawsuit, correct?
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Lawrence D. Loeb
Lawrence D. Loeb@LDLoeb·
@bradmillscan I’m still on 5.7 after a major recovery from 5.5. Waiting for a stable update. Let us know how it goes.
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Brad Mills 🔑⚡️
Brad Mills 🔑⚡️@bradmillscan·
Trying this /goal prompt in Codex to get my OpenClaw fixed one last time before switching to Hermes. Wish me luck 🫡
₿ag Hodlr@agHodlryk

/goal Stabilize my local AI-agent system and get it back to a clean, reliable foundation. Do not assume you know how my system is set up. First discover it. Then map it. Then read the current upstream docs/repos/release notes/issues for every relevant component. Then ask me concise clarification questions for anything ambiguous. Then apply safe incremental fixes with evidence. The goal is a stable runtime, predictable agent behavior, preserved data/integrations, correct wiring, and a final report I can trust. Hard rules: 1. Treat the live machine as the source of truth. 2. Do not expose secrets, tokens, API keys, OAuth files, cookies, bot tokens, or raw credentials. 3. Backup before changing configs, prompts, workspaces, databases, env files, launch jobs, or integration settings. 4. Do not delete, migrate, overwrite, or simplify anything unless you can prove it is unused, broken, duplicated, or superseded. 5. Preserve existing data, memory, history, agents, integrations, credentials, custom workflows, and user-specific behavior unless I explicitly approve a change. 6. Prefer upstream-supported/native config over custom hacks, wrappers, patches, daemons, or sidecars. 7. Do not blindly upgrade everything. Check installed versions, current upstream docs, recent issues, release notes, and rollback paths first. 8. If my intent is unclear, reverse-prompt me with short numbered questions and a recommended default. 9. Do not mark this complete if major items are only planned. Complete means fixed, verified, or explicitly blocked with next action. Phase 1 — Discover and map the system: - Identify all runtimes, CLIs, package managers, repos, config directories, workspaces, agents, prompts, skills, plugins, tools, MCP servers, gateways, memory systems, databases, scheduled jobs, env files, auth profiles, launch services, and integrations. - Find all relevant logs, task/session records, gateway records, memory/compaction summaries, error traces, and recent handoff/audit files. - Produce SYSTEM_MAP.md showing: - what is installed - what is active - what talks to what - what models/providers are used - where memory/data lives - how incoming requests flow to responses - what integrations are wired - what background jobs exist - what is custom vs upstream-native Phase 2 — Read evidence before changing: - Read recent logs and state files deeply enough to understand how the system has actually been behaving. - Identify stuck jobs, silent completions, broken agents, failed tool calls, auth failures, model-routing mistakes, plugin/skill conflicts, gateway delivery failures, memory/compaction issues, slowdowns, and stale config. - Read current upstream docs/repos/release notes/issues for every relevant component before editing it. - Compare my current setup against upstream intent and best practices. - Produce CURRENT_STATE_AUDIT.md with issues ranked P0/P1/P2. Phase 3 — Reverse-prompt me where needed: If you cannot safely infer intent, ask me concise questions before changing anything important. For each question include: - what you found - why it matters - the safest default - what you need from me Do not ask vague questions. Ask only questions that unblock real decisions. Phase 4 — Diagnose root causes: For each issue, record: - symptom - evidence - likely root cause - affected component - upstream-intended behavior - current local behavior - safest fix - rollback path Focus especially on: - agents not answering or not returning results - background tasks finishing silently - broken or slow runtime behavior - model/provider misrouting - gateway/Discord/Telegram/chat delivery problems - memory or compaction losing context - stale or conflicting prompt files - broken tools/plugins/skills/MCP servers - auth/env/config drift - duplicate or shadowed components - custom code replacing native behavior - regressions from recent upgrades Phase 5 — Fix incrementally: - Apply the smallest safe fix for each issue. - Verify each fix before moving on. - Prefer config/doc/prompt alignment over new code. - Preserve custom behavior that is intentional and working. - Do not create a new orchestration layer unless the native runtime truly lacks the needed feature. - Anything questionable should go into TRIM_CANDIDATES.md, not be deleted. Phase 6 — Make agent behavior reliable: Ensure user-originated work always returns one of: - a useful result - an artifact/link/path - a clear blocker - the exact next input needed from me If one agent delegates to another, the original/main agent still owns final delivery back to the user. Use a compact result contract when helpful: status: success | blocked | failed answer: concise useful result artifact: optional path/link proof: optional evidence needs_user: optional exact question Do not add heavy ledgers, dashboards, or tracking systems unless the native runtime has no usable status mechanism. Phase 7 — Validate with smoke tests: Run practical tests that match my actual use. At minimum test: - simple direct request - delegated/subagent request if applicable - long-running/background task if applicable - gateway/chat round trip if applicable - tool/plugin/MCP call if applicable - memory retrieval if applicable - model routing - restart/resume behavior - one real-world workflow I care about For every test, record: - input - expected result - actual result - whether the user got the final answer/artifact/blocker - evidence - remaining issue if any Phase 8 — Align with upstream without destroying my setup: For each important component, record: - installed version - latest stable version - relevant upstream docs/issues - whether to update, pin, rollback, or leave unchanged - why - rollback path Do not migrate me from one runtime/framework to another unless I explicitly approve it after seeing a written migration decision. If migration seems attractive, write MIGRATION_DECISION.md comparing: - what is broken now - whether it can be fixed natively - what migration would preserve or lose - integration/data risk - exact rollback path Phase 9 — Trim only proven deadweight: Classify components as: - active verified - probably active - dormant but useful - duplicate - stale/shadowed - broken - unknown - verified dead Only remove verified dead items after backup. Leave unknown/questionable items in TRIM_CANDIDATES.md for my review. Phase 10 — Final report: Produce FINAL_REPORT.md with: - system map summary - what was broken - root causes - what changed - what was preserved - what was not changed - unresolved blockers - trim candidates - version/update recommendations - smoke-test results - rollback notes - exact next steps Completion criteria: - SYSTEM_MAP.md exists. - CURRENT_STATE_AUDIT.md exists. - FIX_LOG.md exists. - TRIM_CANDIDATES.md exists. - FINAL_REPORT.md exists. - Every P0/P1 issue is fixed or explicitly blocked. - Runtime behavior is stable enough for normal use. - Agents return useful results/artifacts/blockers without silent disappearance. - Important integrations are preserved and verified. - No raw secrets appear in reports. - Final status is one of COMPLETE, PARTIAL, or BLOCKED. Do not call it COMPLETE if major work is only planned.

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Midnight Capital
Midnight Capital@Midnight_Captl·
A couple of my key takeaways from the $NVDA call: 1) standalone CPU biz $20B thru the rest of the year is big, but not massive - still nice add 2) LPU rack is niche product compared to how it was described during GTC (IMO). I specifically asked Jonathan Ross about this and his response made me think it would be a broader use. Curious if there’s been a change in appetite bcuz of rise of agentic use (LPU can’t handle large models or context)? If the Groq acquisition is going to end up being bigger than Mellanox, sounds like there will be quite the road to get there (it’s not bigger than Mellanox day 1 that’s for sure)
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Gavin Baker
Gavin Baker@GavinSBaker·
@bucketshopcap @patrick_oshag I try really, really hard to be nice here on X and everywhere. Turns out the only thing that makes me lose control is a debate on scale-up networking topologies.
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Patrick OShaughnessy
Patrick OShaughnessy@patrick_oshag·
In our last conversation, Gavin said data centers in space will be the most important thing in 3-4 years. He explains that means "racks in space" and thinks orbital compute will solve the watts shortage: "When people hear data centers in space, they picture a Pentagon-sized building in space. That's not what it is. A Blackwell rack weighs 3,000 pounds. It's eight feet high. Four feet deep. Three feet wide. It's racks in space. It has these solar wings that are probably 500 feet long on each side. You keep it in a Sun-synchronous orbit, so those solar panels are always in the sun. And then because it's in an exactly Sun-synchronous orbit, the radiator, which extends behind it for hundreds of feet is in the shade. You link these racks using lasers traveling through vacuum which are already on every Starlink. SpaceX operates the world's largest satellite fleet, which is 98 or 99% of all satellites in orbit. Every Starlink, they're cooling it today. I think Starlink V3 is going to operate at 20 kilowatts. A Blackwell rack is only 100 kilowatts. And people talk a lot about density. Well, if you're connecting the racks with lasers through vacuum, you can make the rack bigger physically. In space, there's all sorts of things that SpaceX can do. They also now operate the largest data center on Earth. I've spent a lot of time at Starbase over the years, and I've talked to a lot of SpaceX engineers. It is the most talented group of engineers on planet Earth, and they're very confident they have solved this."
Patrick OShaughnessy@patrick_oshag

This is my sixth conversation with @GavinSBaker. As always with Gavin, the conversation covers a lot of ground, but we spend the most time on watts and wafers. We discuss: - Why the wafer shortage may prevent an AI bubble - Data centers in space (reframed) - Elon's Terafab and the new chip companies challenging Nvidia - Usage-based pricing - The disaggregation of GPUs - DRAM, frontier tokens, and open source Enjoy! Timestamps: 0:00 Intro 7:55 Anthropic and OpenAI Valuations 12:58 Watts, Wafers, and Infrastructure 14:39 Orbital Compute and Data Centers in Space 22:49 Avoiding the AI Bubble 28:26 Terafab and the Future of US Manufacturing 32:16 Returns to the Frontier 37:23 Continual Learning 42:03 New Chip Companies 48:52 Extending GPU Lifespans and Private Credit 51:22 The Application Layer 57:32 The Token Path and Open-Source Dynamics 1:01:37 Cybersecurity 1:05:46 Diversity Breakdown 1:11:59 Assessing the Big Tech Players in AI 1:19:02 Geopolitics, Personal Safety, and the AI Horizon

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Daniel Newman
Daniel Newman@danielnewmanUV·
$NVDA was an easy call if you are doing the real work and not just staring at charts or reading about 1929 or 1986 or 2008. Channel checks and conversations with customers, partners, ecosystem, competitors. That's the work we do. If you talk to enterprises building with AI the entire revolution makes sense. It is so early. Strap in. The show goes on. 🚀
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Lawrence D. Loeb
Lawrence D. Loeb@LDLoeb·
@firstadopter Knicks had some rust to knock off. Glad they managed to do it before they lost. Biggest comeback since Willis, Clyde, Bradley, DeBusschere, and Barnett.
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