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so glp-1 is basically buddhism in a pill?
learn to market is the new learn to code
Highest potential indie hacker new 🐐 I think especially now that he managed to switch from starter kit to REAL business (TrustMRR) which can be $$$
Where do I start… For a big part of my life, I didn’t fit in. At school, I’d argue with teachers because I didn’t like what I was learning. At university, I got bad grades and drank way too much to build a personality. At my short 9–5 job, I kept wondering what I was doing
Not sure Models keep getting better, then the better models can price themselves more expensive Until equal models arrive for cheaper So it's a highly competitive market I remember Stable Diffusion 1.5 cost about $0.003/image to run but that's because it was open source so only GPU costs Flux changed that and charged per image, or about $0.03/image, so 10x more expensive but better! Then Nano Banana Pro came at $0.15/image, or about 5x more expensive but also way better Now Nano Banana 2 is about $0.05/image so 3x cheaper but worse at some things (editing apparently) but better at resemblance of people (so great for my app Photo AI)
@levelsio That’s crazy man. However do you think that prices will continue to decrease (or AI right now is cheaper that real costs to gain more market space, and in a few years prices will increase)??
View quoted postholy....
Perplexity Computer just came for another $30,000 Bloomberg Terminal feature… POSH<GO>, infamously known as a secret marketplace for the ultra wealthy, was just oneshotted with Perplexity Computer… Private Yachts, Watches, Supercars, Mansions, etc. Data was never the moat.
View quoted postA Vercel user reported an issue that sounded extremely scary. An unknown GitHub OSS codebase being deployed to their team. We, of course, took the report extremely seriously and began an investigation. Security and infra engineering engaged. Turns out Opus 4.6 *hallucinated a public repository ID* and used our API to deploy it. Luckily for this user, the repository was harmless and random. The JSON payload looked like this: "𝚐𝚒𝚝𝚂𝚘𝚞𝚛𝚌𝚎": { "𝚝𝚢𝚙𝚎": "𝚐𝚒𝚝𝚑𝚞𝚋", "𝚛𝚎𝚙𝚘𝙸𝚍": "𝟿𝟷𝟹𝟿𝟹𝟿𝟺𝟶𝟷", // ⚠️ 𝚑𝚊𝚕𝚕𝚞𝚌𝚒𝚗𝚊𝚝𝚎𝚍 "𝚛𝚎𝚏": "𝚖𝚊𝚒𝚗" } When the user asked the agent to explain the failure, it confessed: The agent never looked up the GitHub repo ID via the GitHub API. There are zero GitHub API calls in the session before the first rogue deployment. The number 913939401 appears for the first time at line 877 — the agent fabricated it entirely. The agent knew the correct project ID (prj_▒▒▒▒▒▒) and project name (▒▒▒▒▒▒) but invented a plausible-looking numeric repo ID rather than looking it up. Some takeaways: ▪️ Even the smartest models have bizarre failure modes that are very different from ours. Humans make lots of mistakes, but certainly not make up a random repo id. ▪️ Powerful APIs create additional risks for agents. The API exist to import and deploy legitimate code, but not if the agent decides to hallucinate what code to deploy! ▪️ Thus, it's likely the agent would have had better results had it not decided to use the API and stuck with CLI or MCP. This reinforces our commitment to make Vercel the most secure plat...
truth is that a combo of x premium, youtube premium, an openai or anthropic subscription, plus daily tinkering and consistency, can get you unreasonably far in life right now