Lummox
@Lummox_eth • 7,226 subscribers
developing internet money / dm open
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AI memory is getting f...cking illegal 10 open-source GitHub projects that stop agents from starting from zero EVERY new session 01 Mem0 ▸ → 66K+ stars 02 Hindsight ▸ → retain → recall → reflect 03 memU ▸ TURN MEMORY INTO KNOWLEDGE 04 Cognee ▸ → documents + code + conversations → knowledge graph 05 Graphiti ▸ → remembers how facts change over time 06 OpenViking ▸ MAKE THE AGENT STATEFUL 07 Letta ▸ → memory + identity across sessions 08 Letta Code ▸ REMEMBER ACROSS THE STACK 09 OpenMemory ▸ 10 Agent Memory Benchmark ▸ the loop: experience → remember → connect → retrieve → act → update 3 builds I'd actually test: coding: Hindsight → Cognee → Letta Code personal agent: Mem0 → Graphiti → Letta company brain: Cognee → Graphiti → Hindsight and this is where the sh...t gets interesting: bigger context isn't the same as memory a model can hold 1M tokens and still meet you for the first time every morning these projects change that save this before your agent forgets it ⭣
Lummox313,146 次观看 • 2 天前
I launched Grok Bot for 24 hours, gave it 3 jobs, and stopped treating AI like a chatbot. I had it scan live X + web data for emerging AI products, research what people were actually saying about them, filter the obvious engagement bait, compare sources and turn the strongest signals into a structured brief. At the same time, I could have separate research paths looking at competitors, code or market opportunities instead of forcing everything through one conversation. That’s the difference I underestimated. I don’t want to sit inside a chat writing another prompt every 5 minutes. I want to define the objective, give the agent access to the right tools and data, let it execute, then step back in when judgment or approval is actually needed. The old workflow was prompt → answer → copy → repeat. The workflow I’m moving toward is assign → execute → verify. Once AI stops waiting for your next message, it stops feeling like software you use and starts feeling like work you delegated.
Lummox3,951,979 次观看 • 1 个月前
AI research is getting f...cking illegal 15 GitHub projects for turning a question into sources, evidence, citations, and a finished report 01 GPT Researcher ▸ → recursive deep research 02 STORM ▸ SEARCH DEEPER 03 Perplexica ▸ 04 Open Deep Research ▸ 05 DeerFlow ▸ READ THE WEB 06 Firecrawl ▸ 07 Crawl4AI ▸ 08 Jina Reader ▸ TURN PAPERS INTO EVIDENCE 09 PaperQA2 ▸ → agentic RAG + citations 10 OpenScholar ▸ 11 PaperMage ▸ CONNECT THE SOURCES 12 GraphRAG ▸ 13 LightRAG ▸ 14 Kotaemon ▸ 15 Docling ▸ the loop: ask → split the problem → search multiple paths → read the sources → extract evidence → connect the findings → challenge the answer → cite → write 3 stacks I'd actually try: deep research: GPT Researcher → Firecrawl → GraphRAG → Docling scientific: OpenScholar → PaperQA2 → PaperMage → LightRAG private research: Perplexica → Crawl4AI → Kotaemon → GraphRAG the interesting part isn't generating a 20-page report it's being able to trace where every important claim came from AI already learned how to write this stack teaches it how to research ⭣
Lummox33,660 次观看 • 1 天前
there’s almost no downside and that’s the weird part Elon Musk shared an idea that pulled 27 million views: “stop chasing startups. own boring pieces of the internet and let bots operate them” that’s basically what Grok Bot is doing now. and outside the subscription, the setup costs $0: → make a bot and call it Mogul → give it one job: find a boring niche people search for every single day (it scans X and reddit complaints live, because it’s grok) → it builds a small directory site locally and publishes one useful page every night → it reaches out for affiliate and referral partnerships itself, through your email, under your name → publishing happens automatically on schedule. telegram sends you the revenue reports → month one: almost nothing but traffic. month three: $217. then the pages just keep stacking your laptop can stay closed. the website keeps getting bigger every new page becomes another tiny worker that never clocks out. traffic stacks. revenue stacks. the whole thing compounds some people already run 3 sites like this and barely look at them. they aren’t tweeting about it either. you can probably guess why while you spend another month thinking about which niche to enter, someone else’s bot publishes 30 more pages into it full playbook below save this before your niche isn’t yours anymore
Lummox275,785 次观看 • 26 天前
THE NEXT AI WORKER MAY COST $500 AND SIT BEHIND YOUR MONITOR. Mini PCs are becoming the cheapest path to private AI. A Mac Mini M4 starts at $599, Geekom AX8 Pro starts near $529, and MinisForum pushes up to 128GB RAM for local AI workloads. Ollama turns that hardware into a personal model server. No cloud fee. No data storage bill. No waiting for another SaaS product to decide your limits. Market shift is simple -> computer is moving back to the desk. Owning a mini PC is starting to look less like buying hardware and more like hiring a quiet AI operator.
Lummox394,894 次观看 • 3 个月前
Apple CEO : "Apple just made the Mac Mini small enough to disappear on your desk, but powerful enough to become your AI workstation." The new M4 Mac Mini starts at $599, comes with Apple Silicon, and pushes desktop compute into a box most people could hide behind a monitor. It is the next cheap entry point for local AI, automation, coding, content, and private workflows. Mac Mini + M4 + local models + low power = the new desk setup. Worth more than another $500 productivity course.
Lummox262,119 次观看 • 3 个月前
the agent failed while I was asleep and somehow the work still got finished that was the test I actually wanted i gave GPT-6 Astra a workflow that normally needed me between every step inbound came in messy one agent had to sort it, another find the missing context, another build the deliverable, and the last one check everything before it reached me somewhere in the middle, agent 2 got stuck the source it needed wasn’t there normally that kills the entire chain instead, it wrote the missing piece into a blocker file and passed everything else forward the other agents kept working when I came back, most of the pack was finished and the one thing they couldn’t solve was sitting separately waiting for me that felt more important than a perfect demo because an agent completing 10/10 tasks is impressive an agent failing without taking the other 9 down with it is infrastructure
Lummox19,046 次观看 • 9 天前
a 23-year-old guy built an AI animation factory in one weekend. $12,345 last month. 4 hours of direction total. > Claude writes scripts and scene breakdowns: 10 minutes. > Midjourney generates every frame: 20 minutes. > Runway animates every scene: 15 minutes. > ElevenLabs voices every character: 10 minutes. > Suno scores every episode: 5 minutes. > Make publishes to YouTube, Patreon, and X: 0 minutes. total: $124/month 4 content types running in parallel. 8 uploads per week. he sleeps. it produces. every prompt is in the article above.
Lummox80,087 次观看 • 3 个月前
HERMES AGENT CROSSED 140,000 GITHUB STARS IN 3 MONTHS AND JUST BECAME THE MOST USED AGENT IN THE WORLD. Most AI agents forget everything between sessions. Hermes writes its own skills from experience. Next time it runs the skill, improves it, and gets faster. Independent benchmarks show agents with 20+ self-created skills complete similar tasks 40% faster than fresh instances. Qwen 3.6 where the 35B version outperforms last year's 120B models at one third the memory footprint. DGX Spark with 128GB unified memory running everything locally at $0 per month after hardware. The setup takes 30 minutes. LM Studio plus Qwen 3.6 27B for the model server. One install script for Hermes. One config connecting them. Set context window to 65,536 tokens or nothing works. After one month of daily use your skills directory has 20 to 50 learned workflows. Your Hermes is genuinely different from anyone else's.
Lummox69,824 次观看 • 3 个月前
My Grok Bot turned $1,000 into $3,900 after Elon Musk quoted me. Now someone created a token around what I’m building. I didn’t deploy it. The community did $GrokBottGame. CA: 6py6orPjXSy8GQDkBk2jvW8KQiFHXJ55C58QJ8sypump And strangely, it makes me want to push this experiment even further. I’m still building the trading desk. Still trading. Still breaking agents, rebuilding them and tightening every rule that controls my money. From now on I’ll share the progress publicly: trades, failures, upgrades, numbers and everything I learn along the way. Maybe this becomes more than a trading experiment. Maybe it becomes a community building the whole thing with me.
Lummox14,506 次观看 • 1 个月前
A 19-year-old aimed one AI at a street junction and turned it into $11,000 a month. An old camera, $47 in tools, one weekend of work. YOLO11 boxes every car, motorbike, and pedestrian as it moves, 653 in 5 minutes, and marks the ones running the light. The old count needed a survey crew on the curb with clickers through rush hour. His needed a script behind a camera that was already there. He recorded full video, not a single frame, so the district had nothing to argue with. He brought a flash drive to the district office and asked for 10 minutes. He left with a signed contract, then 5 more clients called. He left every step in the article.
Lummox30,375 次观看 • 2 个月前
A developer opened a law firm’s token bill and realized 20 users were burning $61,920 just to ask questions their files could answer offline. $61,920 cloud TCO. $23,180 local TCO. 4.12-month break-even. 20 employees. 0 client files uploaded. The old RAG pitch breaks when legal files, medical notes, and finance records cannot leave the building. Search snippets are not enough when the firm needs a living wiki of clients, projects, entities, and cross-linked decisions. So the agency installs a local node : OpenClaw watches the intake folder, a local model cleans and links the documents, and Obsidian becomes the private corporate brain. Emails, Slack exports, PDFs, and notes turn into markdown pages the team can browse without sending secrets to an API. That is the real B2B offer. Not “AI chat for your company,” but a private knowledge machine that pays for itself in month 5.
Lummox25,799 次观看 • 2 个月前
![5 GitHub repos with ⭐50K+ combined stars that show how far people are pushing Claude Code beyond a single coding agent 1) instructkr/claude-code 2.3K⭐ -> a research snapshot of Claude Code’s source with the internals exposed for inspection. [ ] 2) codeaashu/claude-code 3.2K⭐ -> another reconstructed Claude Code codebase built for studying how the agent works under the hood, including tools, prompts, MCP. [ ] 3) Donchitos/Claude-Code-Game-Studios 20K+⭐ -> turns one Claude Code session into a game development studio with 49 specialized agents, 73 skills, 12 hooks, 11 rules and 41 templates. [ ] 4) VoltAgent/awesome-claude-code-subagents 24K+⭐ -> 154 specialized Claude Code subagents split across 10 categories. [ ] 5) tanbiralam/claude-code 2.2K⭐ -> a research-oriented Claude Code source reconstruction for inspecting the terminal agent architecture, plugins, tools and internal implementation. [ ] Save these if you want to understand what people are actually building around Claude Code instead of just using the default CLI.](https://image.24vids.com/tw-2090119203810320462/amplify_video_thumb/2090119067331956736/img/tm1pipu-4wl7-_AJ.jpg)
5 GitHub repos with ⭐50K+ combined stars that show how far people are pushing Claude Code beyond a single coding agent 1) instructkr/claude-code 2.3K⭐ -> a research snapshot of Claude Code’s source with the internals exposed for inspection. [ ] 2) codeaashu/claude-code 3.2K⭐ -> another reconstructed Claude Code codebase built for studying how the agent works under the hood, including tools, prompts, MCP. [ ] 3) Donchitos/Claude-Code-Game-Studios 20K+⭐ -> turns one Claude Code session into a game development studio with 49 specialized agents, 73 skills, 12 hooks, 11 rules and 41 templates. [ ] 4) VoltAgent/awesome-claude-code-subagents 24K+⭐ -> 154 specialized Claude Code subagents split across 10 categories. [ ] 5) tanbiralam/claude-code 2.2K⭐ -> a research-oriented Claude Code source reconstruction for inspecting the terminal agent architecture, plugins, tools and internal implementation. [ ] Save these if you want to understand what people are actually building around Claude Code instead of just using the default CLI.
Lummox12,775 次观看 • 1 个月前
JIM SIMONS NEVER HAD A LOSING YEAR IN 34 ATTEMPTS. HERE IS THE 5 LAYER PIPELINE BEHIND IT. Spring 1988. A Cold War mathematician fired every trader and replaced them with signal detectors. $60 billion in performance fees. 39% annual returns after a 44% fee. Zero losing years. Layer 1 : Hurst exponent. Below 0.45 fades work. Above 0.55 momentum works. Medallion switches strategy automatically based on regime. Layer 2 : Factor decomposition. Renaissance runs 200+ factors privately. High alpha plus low R-squared means your edge is unexplained by known factors. Layer 3 : Markov Chain regime modeling. 76% Bear probability over 5 periods means no long positions. The model decides before you do. Layer 4 : Neural net. Three outputs : long, short, flat. Not predicting price. Predicting direction probability. 52% accuracy Kelly-sized correctly still prints money. Layer 5 : Execution. Half-Kelly sizing. Hard 2% risk cap. Edge must exceed execution cost by 1.5x minimum. This is where 80% of quants bleed out. 400 people. One shared model. The edge was never a single formula. It was always the pipeline.
Lummox24,152 次观看 • 3 个月前
21 videos per week. 150,000 views daily. $3,000-$10,000 a month YouTube's algorithm does not reward the most creative creators. It rewards the ones who publish 4 times a week without breaking down. One video takes a full day. Description alone is 45 minutes. Multiply that by 4 uploads per week and the math breaks you. Most channels pay not because the content is weak but because the creator runs out of bandwidth. Claude solves the production bottleneck directly. Feed it your raw idea and speaking points. A tech channel publishing twice per week earns around $300 monthly at 50,000 views. The same creator using Claude to publish before a single sponsorship lands. The content does not get worse. YouTube interprets that as authority and pushes the channel harder.
Lummox19,789 次观看 • 3 个月前
THE CHINESE INSTALLED 6 AI AGENTS RUNNING 24/7 FOR $11 A MONTH. THE BOX UNDER ONE DESK REPLACED $1,200 IN CLOUD SUBSCRIPTIONS. The Minisforum MS-S1 Max ships with 128GB of unified memory shared between CPU and GPU. Qwen3-Coder 30B runs at 40 to 50 tokens per second. 6 agents run simultaneously without anyone watching the meter. An inbox sorter drafts replies every 15 minutes. A research monitor watches 30+ feeds and lands a digest by 7 AM, a job that costs $15 to $20 a day on cloud APIs. The same stack on cloud subscriptions ran $800 to $1,200 a month. The hardware costs $3,000 once. Break-even hits month 3 to 4. The real shift is behavior, not savings. A meeting prep agent never would have justified per-token cost. Running it free overnight made it obvious.
Lummox14,093 次观看 • 3 个月前
One iPhone with iOS 18.2. A Gumroad account. $18,200 in 12 months. Apple Intelligence wrote 80% of the first drafts. The formula is simple : > AI creates the raw material > you package and price it > the internet distributes. 5 revenue streams built on free platforms. Prompt packs on Gumroad generated $6,840. A weekly newsletter on Substack brought $4,320. One ebook on Amazon KDP added $3,060. Medium articles paid $2,190. Notion templates closed the gap at $1,830. The fastest product to ship is a prompt pack. 30 to 50 tested prompts for a specific use case, $9 to $15 on Gumroad, 3 hours to build. One guide published in month 4 made $340 in 90 days from 2 hours of work.
Lummox13,989 次观看 • 4 个月前
NVIDIA is creating two completely different futures In one they put AI infrastructure on your property In the other they put it on your own desk ///// OPTION 1 A box worth more than most houses gets installed outside You provide the location, power and connectivity In return, you get perks like cheaper electricity, backup power and upgraded internet while AI workloads run around the clock You're not buying the hardware You're hosting it ///// OPTION 2 You spend a few thousand dollars on a DGX Spark Small enough to sit beside your monitor Powerful enough to run serious AI models locally No cloud bill No rented GPUs No sending sensitive data somewhere else The interesting part isn't the hardware It's the shift that's happening For years, AI belonged to giant tech companies and massive data centers Now NVIDIA is pushing a world where regular people can either host the infrastructure or own a piece of it themselves
Lummox10,019 次观看 • 4 个月前
Another Chinese student made $400K for last 30 days He used Markov Chains and Claude Even without little knowledge you can make money on it How it works : > Markov Property : future depends only on the current state > State Space : the set of all possible states in the system > Transition Probability : the chance of moving from one state to another > Transition Matrix : a table of all transition probabilities between states > Steady State : the long-run equilibrium distribution as steps → infinity Just using these 5 rules you will be able to trade with 55%-65% wr Chinese trader has 71% wr and $418k PNL Account : Top formulas for trading so : 1) The Markov Property RSI = 100 - (100 / (1 + RS)) RS = Avg Gain / Avg Loss (N=14) RSI > 70 → Sell | RSI < 30 → Buy 2) State Distribution SMA = (P1+P2+...+PN) / N EMA = P×k + EMA_prev×(1-k), k = 2/(N+1) Fast MA crosses UP → Buy (Golden Cross) Fast MA crosses DOWN → Sell (Death Cross) 3) Expected Value EV = (p × W) − (q × L) p = win prob q = 1 − p W = profit if win L = loss if lose My result is 66,3% and $780 profit for last week I keep on learning trading every day and going to make my first $10k Trade with me :
Lummox11,242 次观看 • 5 个月前
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