New fuzzy matching

Intuition of chatGPT with join capabilities of fuzzy matching and confidence scores

@PaulSkallas x.com/goodlookup/sta…

Possibly the only lindy software program ever made. Because it resembles something much older The ledger, the grid, the table. Merchants, monks, and bureaucrats have been keeping rows and columns of numbers for thousands of years, on clay tablets in Mesopotamia, wax tablets in Rome, account books in Florence.

@deedydas This is the pagerank moment

Rich Sutton just published his most important essay on AI since The Bitter Lesson: "Welcome to the Era of Experience" Sutton and his advisee Silver argue that the “era of human data,” dominated by supervised pre‑training and RL‑from‑human‑feedback, has hit diminishing returns; the future will belong to agents that — act continuously in real or simulated worlds, — generate and label their own training data through interaction — optimise rewards grounded in the environment rather than in human preference alone, and — refine their world‑models and plans over lifelong streams of experience.

@nathanbenaich Memorize infinity

frontier ai today

@ContrarianCurse Relevant

@nikitabier The AI knows how to do distribution

The entire tech community is under the impression that AI coding will result in power flowing from engineers to “idea guys.” Wrong—it will always flow to whatever still has scarcity: those who know how to get distribution

i was procrastinating doing my taxes so we built cursor for excel

Gemini now works in google sheets A success that Microsoft would also like to have with Office 365 Via Reddit

@businessbarista Also network effects

I'm increasingly convinced most software can be rebuilt in days/weeks thanks to AI. If I'm right, only 4 things will give you a sustained moat: 1) taste - combo of customer obsession & having a high-functioning UX/UI-meter 2) speed - combining an aggressive product roadmap with a drop-style marketing playbook that leaves people saying "i can't believe how fast they ship" 3) data - building memory that makes your product better every single time a user signs in 4) switching cost - "starting from scratch would be a nightmare" - how confident are you that your customer would say this?

1. Mckinsey Consultant as a Software 2. Venture Capitalist as a Software 3. FP&A Analyst as a Software 4. Legal / Compliance as a Software Build and make it widely accessible. If you want credits, reply to this post. If there are other ideas you want to pursue and need credits, reply again.

@CCM_Brett Now do $UBER

Make it make sense

@yacineMTB Related party round tripping

so I invest in an AI company based on an agreement that they'll rent our GPUs and TPUs. That way, we increase our own revenue while owning an asset that increases in valuation. It's free money

@danshipper @every “Trawl through recent 10ks to find unreported financial irregularities”

OpenAI just launched an autonomous research assistant, Deep Research. We've been testing it for a few days @Every and it's like a bazooka for the curious mind: - Give it a question, and it will autonomously search the web (or provided sources) to compile an answer - It does this over many turns—taking between 1 and 30 minutes to return a response - It returns MASSIVE well-researched reports, synthesized from many different sources, sometimes running 10k+ words Think about it like a double decker tour bus but you’re the only passenger and the city you’re touring is the sum total of human knowledge A few things we had it do: - Write a comprehensive history of Every from 2020 to today  - Read chapter 1 of War and Peace, analyze Tolstoy's character descriptions, and tell us what that says about his view of human nature - Trawl through recent 10ks to find unreported financial irregularities - Research and compile a completely new wardrobe from a few photos Of course, there are limitations: - Sometimes it doesn't fully cite where a piece of information came from - There's no "stop" button yet, so if it's going off the rails you have to start over But it's very clearly a peak into the future of human-AI collaboration for knowledge work. Exciting times!

The real moats will be distribution, network effects, and user interface… same as it ever was

Not sure that deepseek can move the market… the market is about enterprise and would be hard for deepseek to be trusted by enterprises.

@FoundersPodcast Would it be fair for Grover Cleveland and Donald Trump to be analyzed in a similar way to Durant, Jobs and Chanel?

@goodlookup Good question Not the same as Durant or Jobs but perhaps Coco Chanel —by creating a civil war with her partners over compensation

@FoundersPodcast Question: are Steve Jobs and William Durant the only two examples of founders losing then regaining control of their companies? Is there anyone else you’re aware of?

some day the piecing together of dissociated knowledge will open up such terrifying vistas of reality, and of our frightful position therein, that we shall either go mad from the revelation or flee from the light into the peace and safety of a new dark age

@drew_ponder @EricRWeinstein @ylecun Delve

Let's delve into the debate surrounding Eric Weinstein's @EricRWeinstein critique of Yann LeCun's tweet on the scientific study of misinformation. We'll explore both Weinstein's criticisms and potential counterarguments to provide a balanced perspective. ________________________________________ Yann LeCun's Position Yann LeCun, a prominent figure in the field of artificial intelligence, posits that: 1.Scientists Lean Left Because They Care About Facts: LeCun suggests that the scientific community generally leans left politically because of a commitment to factual accuracy and evidence-based reasoning. 2.Misinformation Primarily Originates from the Right: He further asserts that misinformation predominantly stems from right-leaning sources, making it a focal point for left-leaning scientists to study and combat. ________________________________________ Eric Weinstein's Critique Eric Weinstein challenges LeCun's assertions on several fronts: 1.Lack of Scientific Rigor: Weinstein argues that LeCun's claims are extraordinary yet unsupported by empirical evidence. He questions the methodology behind such sweeping generalizations. 2.Oversimplification of Political Dynamics: By attributing the political leanings of scientists solely to a commitment to facts, Weinstein contends that LeCun overlooks the complex interplay of political economy, funding structures, and institutional dependencies that influence scientific research. 3.Potential Bias and Strawmanning: Weinstein accuses LeCun of employing an appeal to authority and possibly strawmanning by suggesting that misinformation is predominantly a right-wing issue without acknowledging instances where the institutional left has also propagated inaccuracies. 4.Historical and Structural Context Ignored: He points out that factors like the Mansfield Amendment, incentive structures within academia, and historical shifts in scientific paradigms are absent from LeCun's analysis, which weakens the argument's foundation. ________________________________________ Supporting Weinstein's Critique 1.Empirical Evidence Required: Weinstein is correct in emphasizing the need for empirical support when making broad claims about political leanings and sources of misinformation. Without data, such assertions remain speculative. 2.Complexity of Scientific Funding: Scientific research is often influenced by funding sources, which can introduce biases irrespective of the scientists' personal beliefs. Ignoring this aspect can lead to an incomplete understanding of the motivations behind research trends. 3.Diverse Sources of Misinformation: Misinformation is not exclusive to any single political spectrum. Highlighting only one side can undermine efforts to comprehensively address the issue and may reflect confirmation bias. 4.Historical Precedents: The history of science includes instances where prevailing scientific consensus was later overturned, suggesting that institutional biases can exist on multiple ends of the political spectrum. ________________________________________ Counterarguments Supporting LeCun's Position 1.Observable Trends: LeCun may be referencing observable trends where certain scientific communities, particularly in social sciences and technology, exhibit left-leaning tendencies. This could stem from shared values like equity, diversity, and skepticism of unchecked power, which align with leftist ideologies. 2.Nature of Misinformation: The assertion that current prominent misinformation campaigns are primarily right-wing could be based on the visibility and impact of such movements, especially in the context of recent political climates and social media dynamics. 3.Commitment to Facts: The scientific method inherently values evidence-based conclusions. LeCun's point might be that a commitment to uncovering and disseminating facts naturally aligns with progressive ideals that prioritize truth and transparency. 4.Practical Focus: LeCun's statement might aim to highlight a practical reality within specific domains of misinformation research rather than making an absolute claim about all scientific endeavors. ________________________________________ Conclusion Eric Weinstein raises valid concerns about the need for rigorous evidence and the dangers of oversimplification in scientific discourse. His critique underscores the importance of considering a multitude of factors, including political economy and historical context, when evaluating claims about scientific biases. On the other hand, Yann LeCun's assertions may reflect observable patterns within certain scientific communities and contemporary misinformation challenges. While his statements might benefit from a more nuanced exploration, they also highlight real issues that merit discussion. Ultimately, the debate emphasizes the necessity for transparent, evidence-based analyses in understanding the interplay between science, politics, and misinformation. Both perspectives contribute to a deeper conversation about maintaining integrity and objectivity within scientific research.

This *is* what science looks like right now. Does this sound like real science? Even at a passing level? Just see. Read it. “People studying misinformation lean left for two reasons:” Extraordinary claim. Supported by….? I mean…Huge if true! I would have thought there would be complicated effects of political economy in science funding as well. But there is no discussion of any such effects. It’s just two causes. Who knew. “1. scientists lean left, regardless of specialty, because they care about facts.” I mean….damn. I don’t even understand the argument. It feels like “because” is doing all the work here. No discussion of history (e.g. The Mansfield Amendment), incentive structures, institutional dependence. Just a bald assertion known as an appeal to authority. The author is a professor, after all. . “2. misinformation today primarily comes from the Right ("they're eating the dawwwgs!") which makes it worth studying and fighting against for people leaning left.” Appeal to ridicule. Strawmanning. Yes, Donald Trump is no scientist. But the Institutional Left has been wrong all over the place, no? On sex, heritability, public health, viral origins, migration externalities, and prediction of elections via failure to adjust for preference falsification at scale. What is this? I don’t know. It’s not the science you grew up witb that changed everything and illuminated the world. My point is not to vilify Dr LeCun. It is to point out what institutional science NOW looks like. It used to look totally different. But in 2024, it looks like exactly like this. This tweet ⬇️ below. Learn to spot it.

@MikeyOver1 4D Gaussian Splatting

Don’t know if it’s the camera angle or the play But I might be out on Caleb Williams

@TheHumanoidHub @AtomLimbs This could eventually be training data for robots

AI and robotics is on the verge of transforming prosthetic limbs. Watch @AtomLimbs' tech in action.

@awkwardgoogle Train a model with this

I know topology is real, but my brain is still trying to catch up.