Daniel Fabbri (@dfabbri) profile picture
Nashville, TN
Joined April 2009
Photo by Daniel Fabbri (@dfabbri)Photo by Daniel Fabbri (@dfabbri)Photo by Daniel Fabbri (@dfabbri)Photo by Daniel Fabbri (@dfabbri)Photo by Daniel Fabbri (@dfabbri)Photo by Daniel Fabbri (@dfabbri)Photo by Daniel Fabbri (@dfabbri)Photo by Daniel Fabbri (@dfabbri)Photo by Daniel Fabbri (@dfabbri)Photo by Daniel Fabbri (@dfabbri)
Daniel Fabbri retweeted
Engineering is now this: new agent -> shift+tab -> wispr plan -> wait -> look at X -> review plan -> make adjustments -> approve/build -> verify it works -> integrate tests -> merge -> go to app
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Daniel Fabbri retweeted
Anyone who’s written software knows that the last 5% can take 95% of the time Edge cases, conflicts, weird environments, you name it And for many areas of work, 95% right is still 100% wrong AI is a remarkable tool and a huge productivity gain. Laws of physics still apply.
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Daniel Fabbri retweeted
Jensen Huang: Market is wrong about software stocks "The notion that AI is somehow going to replace software companies is the most illogical thing in the world and time will prove itself" Interview date: 3 February 2026
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Daniel Fabbri retweeted
We just shipped one of the coolest free database perf tools It's a SQL execution plan visualizer which helps you spot things like a missing index, full-table scan, or inefficient join Just paste the EXPLAIN output and find out why your query is slow: explain.datadoghq.com
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Daniel Fabbri retweeted
What a crazy week it’s been here in Nashville. Almost can’t believe what this town (and I know many others in this region) have gone through. The ice storm was devastating and will take months to clean up and years to recover from. Our street look like a bomb exploded-complete devastation! Our area is on Day 6 without power-there are many who are really struggling and need help but streets are shut down and it’s hard to get to everyone. Nashville will eventually recover and comeback stronger but this is still disaster area. Really appreciate all the Good Samaritans (like my buddy Steve Hannah) and Arborists that are out trying to help as many as they can. If you have family or friends in this southern region I pray their finding warmth and a place to stay-temperatures this weekend here are going to still be well below freezing. Stay SAFE & Stay STRONG
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Daniel Fabbri retweeted
I took the map of Nashville power outages (as of 4pm) posted by NES and made it a little clearer by overlaying major roads:
This map highlights restoration efforts since Sunday. Areas where power has been restored are marked in green, while areas still experiencing outages are indicated in red as of 1.28.26 at 4 p.m. Our teams are navigating tough conditions and broken poles, working extended shifts to ensure the quickest possible response. We know the wait is difficult, but we see you, we hear you and we won’t stop until everyone is green.
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Daniel Fabbri retweeted
Claude is great at coding. Like really, really good, compared to using a regular LLM like Gemini. There's only one issue with it. Because it never sees the full code of the app but uses grep search for relevant code snippets, it's myopic. If grep returns a fragment of code similar to the bug description, it often doesn't look further and fixes an irrelevant part of the app or answers a question based on these fragments found by grep. So, as the codebase grows, it becomes important for the user to know the codebase. Otherwise, Claude will reinvent the bicycle over and over again, creating duplicate implementations for the same functionalities in different places in the app. This issue is probably fixable with additional finetuning, but right now this is how it works.
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Daniel Fabbri retweeted
After rewatching Home Alone, I couldn’t stop wondering: how plausible is the oversleep that leaves Kevin behind? So I wrote a tiny paper and ran the numbers. Merry Christmas! 🎄
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Daniel Fabbri retweeted
After reading Karpathy's YC talk and year-end review, it's clear that there's a real, defensible layer between foundation models and end users. The pattern he calls out is "Cursor for X". It's what Cursor revealed about how LLM apps should be architected. Karpathy identifies four things these apps do: 1. Context engineering. The app decides what goes into the context window. You don't manually copy-paste code files and error logs. The app does the retrieval, embedding, and curation. This is a ton of hidden work. 2. Multi-call orchestration. Under the hood, there are embedding models for your files, chat models for reasoning, models that apply diffs. The user sees one experience. The app runs a whole orchestra. 3. Application-specific GUI. This is undersold. Text is hard to audit. Seeing red/green diffs uses your visual system, which is way faster than reading. Command+Y to accept, Command+N to reject. You're not typing "yes I accept this change" into a chat box. 4. Autonomy slider. Cmd+K changes a small chunk. Cmd+L changes a file. Cmd+I does more autonomous work. The user controls how much the AI does at once. The human verification step is the bottleneck. AI generates instantly. But you're still responsible for the output. If you get a 1,000 line diff, you have to verify it actually works, introduces no bugs, and has no security issues. That takes time. So there are two levers: - Speed up verification (GUIs, visual diffs, good UX) - Keep the AI on a leash (smaller chunks, clearer prompts) If your prompt is vague, AI does something unexpected, verification fails, you re-prompt. You're now spinning in a loop. Better to spend more time on a precise prompt that increases the probability of successful verification on the first pass. "This is the decade of agents" Right now, with where models are, you want suits. Partial autonomy products where the human stays in the loop, the generation-verification cycle is fast, and there's an autonomy slider you can push right over time. Suits augment. Robots operate autonomously. The suit still has a human making decisions. The robot flies around on its own. Build Iron Man suits, not Iron Man robots.
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Daniel Fabbri retweeted
Warren Buffett signs off his final investors’ letter with advice for the ages.
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Daniel Fabbri retweeted
Why can AIs code for 1h but not 10h? A simple explanation: if there's a 10% chance of error per 10min step (say), the success rate is: 1h: 53% 4h: 8% 10h: 0.002% @tobyordoxford has tested this 'constant error rate' theory and shown it's a good fit for the data chance of success declines exponentially
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Every day for the next 10 days is a palindromic date (the same number backwards): 5/20/25 5/21/25 5/22/25 5/23/25 5/24/25 5/25/25 5/26/25 5/27/25 5/28/25 5/29/25
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"The moat in AI products is built through 1,000 tiny paper cuts" - @rossmcnairn, cofounder at Wordsmith If you want a moat with an AI product, you need to iron out all the tiny details that annoy users with your product. Anyone can copy an AI product. Not anyone can copy one pleasant to use that "just works" just as expected
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Daniel Fabbri retweeted
“Everyone is going to make their own apps with AI” My friend you don’t even make your own food. You’ll still pay a few bucks to use an app.
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Daniel Fabbri retweeted
Actual traits of successful founders: Good at building. Genuinely interested in the problem. Very determined. Talks a lot to users.
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“Craft is knowing how to start, art is knowing when to stop”. Did not have Ben Affleck’s astute commentary about AI on my bingo card.
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Knowledge Management for RFP Response Generation 📑 Generating simple chat responses is not the only use case for indexing your documents in a vector database - these retrieval endpoints can be used by LLM agents to achieve complex agentic workflows, such as artifact generation! In our new example we show you how to use LlamaCloud to index an entire bucket of unstructured data, letting a research agent do chunk-level and document-level research to find the relevant context for RFP report generation. Cookbook: github.com/run-llama/llamacl… LlamaCloud Signup: cloud.llamaindex.ai/ If you’re interested in knowledge management for your LLM agents, come talk to us: llamaindex.ai/contact
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Building a platform to make LLMs easy to use is a very different skill than training a model. The divide has a lot of parallels to microchips / operating systems. We're already starting to see the emergence of a new runtime infra layer.
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