MasteringMachines AI

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MasteringMachines AI

MasteringMachines AI

@MstrMachines

Discovering Tomorrow's Reality. A platform that highlights the innovation and absurdity of the AI industry.

Beigetreten Mart 2023
166 Folgt213 Follower
MasteringMachines AI retweetet
Kyle Asay
Kyle Asay@KyleAsay_·
Levels of AI psychosis: Level one: You believe Claude's/ChatGPT's praise for you/your work is sincere and not just a ploy to keep you using the product Level two: You measure your output in number of lines of code/number of github pushes (even though you did none of the work) Level three: You stop caring about revenue/profit/growth and instead brag about money spent on AI tokens Have seen level one and two for a while. Seeing level three quite a bit more with founders posting screenshots of their Anthropic/OpenAI invoices to "brag" about how much money they are spending. Bizarre world.
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MasteringMachines AI
MasteringMachines AI@MstrMachines·
GIF
How To AI@HowToAI_

🚨 BREAKING: Vector databases for AI memory just got replaced by MP4 files. Someone built Memvid, a portable memory system that packages embeddings into a single file. It stores millions of text chunks using video encoding logic for sub-millisecond retrieval. → Replace expensive vector databases with single file. → Lightning-fast semantic search without a server. → Portable, versioned, and crash-safe AI memory. 100% open source.

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Luke Pierce
Luke Pierce@lukepierceops·
I mapped every AI automation opportunity across 25 industries. 10-15 pain points each. With the exact positioning, pricing range, and who to sell to. This took me 4 years and 80+ client engagements to figure out. A lot of AI agencies pick a niche and pray. They don't know the actual pain points. They don't know who the buyer is. They don't know what these companies are already paying for broken solutions. They don't know what the realistic project size is. So they end up competing on price for generic "AI automation" gigs. I've worked with marketing agencies, recruiting firms, e-commerce brands, law firms, real estate companies, healthcare practices, financial services, SaaS companies, manufacturing, construction, logistics, and more. Every single one has 10-15 processes that are bleeding money because they're still done manually. Here's what the guide covers for each industry: → The top 10-15 automation pain points (ranked by ROI) → Who the actual buyer is (CEO, COO, ops manager, etc.) → What they're currently paying for manual labor or broken SaaS → Realistic project pricing ($5K-$60K+ depending on scope) → The discovery questions that unlock the deal → How to position yourself as the expert even if you've never worked in that industry → Red flags to avoid (industries and company sizes that aren't worth it) 25 industries and 300+ specific automation opportunities. This is the cheat code for picking your niche and knowing exactly what to sell before you ever get on a call. Like + RT + reply "NICHE" and I'll send you the full guide (Must be following so I can DM)
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MasteringMachines AI
MasteringMachines AI@MstrMachines·
@simonw is goated.
Lenny Rachitsky@lennysan

"Using coding agents well is taking every inch of my 25 years of experience as a software engineer, and it is mentally exhausting. I can fire up four agents in parallel and have them work on four different problems, and by 11am I am wiped out for the day. There is a limit on human cognition. Even if you're not reviewing everything they're doing, how much you can hold in your head at one time. There's a sort of personal skill that we have to learn, which is finding our new limits. What is a responsible way for us to not burn out, and for us to use the time that we have?" @simonw

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Alex Volkov
Alex Volkov@altryne·
Uh Oh... @AnthropicAI official response to everyone burning through their sessions in SessionGate is.. You're holding it wrong? Come on! Their recommendation is to: > Don't use Opus if you're on Pro > Don't use 1M context (they cost more despite anthropic setting them as default!?) > Do not resume large sessions after 1hr (not acknowledging the potential cache busting bug?) > Claim that no-one was overcharged. > No quotas reset (unlike Codex folks) I'm sure that this won't go well with the thousands of folks who experience significant decrease in the ability to use their Pro/Max plans and are cancelling in favor of other solutions. I don't want to dunk on Lydia, she's one of the few folks from Anth who actually acknowledged the community, please don't take this out on her, but continue voting with your wallets and sending them very quick sessions that eat most of your quota with /feedback people!
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Lydia Hallie ✨@lydiahallie

Thank you to everyone who spent time sending us feedback and reports. We've investigated and we're sorry this has been a bad experience. Here's what we found:

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MasteringMachines AI
MasteringMachines AI@MstrMachines·
The running directly on device part makes it so much more accessible.
Chubby♨️@kimmonismus

To explain why I consider Gemma 4 a bigger release than most people realize. This is a big deal because models like Gemma 4 E4B can run directly on devices, bringing powerful AI (even a 2B model ~60% on MMLU Pro) to phones, laptops, and edge systems without relying on the cloud, making it faster, cheaper, and more private. At the same time, their strong performance on everyday benchmarks shows that useful intelligence no longer requires massive infrastructure. We’re entering a phase where capable AI becomes ubiquitous, embedded into everyday devices rather than locked inside data centers. And the jump from Gemma 3 to Gemma 4 further demonstrates how well such small models will perform in another 12 months. Today they have GPT-4o levels. In a year, perhaps GPT-5 levels. We will see.

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MasteringMachines AI
MasteringMachines AI@MstrMachines·
Spot on 🎯
Robert Scoble@Scobleizer

Media shifts big time today. Why is @tbpn getting bought by OpenAI important? (Technology Business Programming Network) Because media is about to shift HARD to being done by AI. Look at the news site I turned on yesterday. 100% built by AI. alignednews.com/ai It already wrote about the news and says "it's a big day." But there are bigger shifts coming. AI, like the site/system I built, will: 1. Watch the news on services like X, and elsewhere. 2. Write about it. 3. Come up with its own reporting (something like @boardyai will be built to call sources and do its own reporting, like calling the local fire chief after a big fire to get more details). 4. Build a new kind of 24-hour-a-day personalized news show. My site already gives the basics, the AI I used built an MCP server, an OpenClaw feed, a way to create a podcast on @NotebookLM, an email newsletter, and an RSS feed. All built by two people (me and @blevlabs) with about $10,000 investment. And it costs a few hundred dollars a day to run. Soon we'll have a show up on @HeyGen too. Grok can already simulate any conversation between me and anyone else. Here it has me interview @jordihays one of the cofounders of the network: grok.com/share/bGVnYWN5… about the future of media. Took two minutes to do. Now take that over to @NotebookLM and it'll create a video, a slide deck, a mind map, an audio podcast. Here I did it for you: notebooklm.google.com/notebook/d5051… It is creating a video as I talk. And the podcast is highly interesting on this topic. All built in minutes. The news isn't even an hour old yet. It created a slide deck, that I took the graphic for this post from. Now what does TBPN have? A great library of the biggest AI thinkers. It's been interviewing the top CEOs every day for more than a year. That dataset gives TBPN and OpenAI a huge dataset to train new models to do new kinds of journalism and create a 24-hour-a-day TV channel that's almost wholly AI generated. Or at very minimum AI produced. My AI already tells me everyday who I should interview. Theirs will too. And take care of all the grunge work to setup the show, call the guests, prepare them for being on air, and schedule everything out. Even there AI can help viewers who don't have time to watch all the interviews. It can automatically clip out pieces of the interview, and present them to people in a highly personalized way. Someone interested in medical companies would only see news for them and that would be different than news presented to someone who cares about automotive news, for instance. This slicing and dicing is huge. At GTC I talked to @furrier, founder of another news network. He has a similar dataset, since his company does interviews at many of the big technology shows. He told me it's his dataset that has value. He's built a similar AI system that can cover news in a much more intelligent way than if you don't have that kind of database of thousands of tech interviews. It also gives OpenAI a way to make sure its point of view is distributed to everyone. And that will get more important soon as Google, Meta, Apple and others bring "AI glasses" that let you see the news in a whole new way. Google's glasses arrive in October. I keep hearing OpenAI is working on some too, but even if it decides not to, it will be an important AI on the others and it will bring those users this personalized news system, and other kinds of content too. 18 months from now this whole system will be built. Every journalism firm will need to do the same to survive. Journalism outlets need AI partners. And fast. Or they will get completely locked out. That's why media just had a major shift today.

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MasteringMachines AI retweetet
Hedgie
Hedgie@HedgieMarkets·
🦔 About half of the US data centers planned to open in 2026 are expected to be delayed or canceled, according to analysts at Sightline Climate. Of the 12 gigawatts of data center capacity announced for this year, only a third is actually under construction. The problem is not money or ambition but electrical components. Transformers, switchgear, and batteries make up less than 10% of data center construction costs but are impossible to build without. US manufacturing cannot meet demand so builders have been sourcing from China, Canada, Mexico, and South Korea. Transformer delivery times have stretched to as long as five years. US imports of high-power transformers from China jumped from fewer than 1,500 in all of 2022 to more than 8,000 in the first ten months of 2025 alone. My Take This is the physical reality underneath the AI infrastructure announcements and it deserves more attention than it gets. Hyperscalers have committed over $650 billion in AI infrastructure spending this year. The Atlantic piece I shared last week laid out how the supply chain for this buildout runs directly through the Middle East and China. This story is the electrical component version of the same problem. The US cannot build the data centers it has announced because it cannot manufacture the parts fast enough, and the parts it can get come primarily from China, the country it is simultaneously trying to decouple from through tariffs. The numbers get more difficult the further out you look. For data centers planned to open in 2027, only 6.3 gigawatts are under construction against 21.5 gigawatts announced. For 2028 through 2032 the vast majority haven't broken ground. There is a growing gap between what has been promised to investors and what is physically being built, and that gap sits underneath valuations that assume the buildout happens on schedule. Hedgie🤗
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