cesar ai @caesar_aii

JACK HUMANOID ROBOTS AT JUKI SEWING MACHINES CAN TARGET REPEATABLE FABRIC HANDLING, BUT THE REAL TEST IS WHETHER THEY CAN RECOVER WHEN CLOTH SHIFTS The JACK units in this video sit at standard JUKI sewing machines. Their hands guide fabric where a garment worker normally would. That is the real automation target: repeatable fabric handling. It does not require a fully dark factory. It starts with one station that can be measured and trained. Sewing is hard because soft material bends, slips, and changes shape. A robot needs more than a precise arm to manage that work. It needs vision, force control, and recovery when cloth shifts. That makes a sewing line a tougher test than a polished warehouse demo. The work is not just moving an object from one place to another. The robot has to keep the material aligned as it changes under its hands. If these systems complete narrow garment steps reliably, work can shift. Factories can redesign roles around setup, quality checks, and exceptions. Humans would handle the cases that break the routine. The footage is not proof of production-scale deployment. It does show where the race is heading: ordinary machines and messy tasks. The important test is whether robots can repeat this work beyond the demo. Bookmark this before you need the workflow. Follow for the next breakdown worth keeping.

danikkk_wqs @danikkk_wqs

9 hours ago

x.com/i/article/2086…

0 0 23 5K 14

LOOP ENGINEERING CAN TURN CLAUDE CODE INTO A SYSTEM THAT CHECKS RESULTS, FINDS GAPS, AND STARTS THE NEXT USEFUL TASK Prompt engineering tells a model what to do in a single exchange. Context engineering lets it fetch files, tools, and MCP data when needed. Harness engineering keeps long tasks organized outside the model's chat window. It can split requirements, preserve progress, and keep a coding run on track. Loop engineering adds a layer that decides what the harness should do next. The agent can inspect a result, find a gap, and start the next useful task. That is the difference between asking Claude Code to clone a site once. And operating a system that checks the site, updates it, and fixes regressions. Addy Osmani's version names six building blocks for that loop. They are Automations, Worktrees, Skills, Plugins, Sub-agents, and State. Automations trigger work without a fresh prompt from you every time. Worktrees isolate changes so parallel tasks do not overwrite each other. Skills and plugins give the agent repeatable procedures and outside connections. Sub-agents divide the work, while state prevents every run from starting blind. The limitation is simple: an autonomous loop can also automate bad judgment. More agent turns can mean more tokens, more churn, and faster production of mistakes. The useful test is not whether the loop runs overnight. It is whether it has clear checks, bounded permissions, and a measurable job to finish. Bookmark this before you need the workflow. Follow for the next breakdown worth keeping.

Jimmy Neuron 💡 @Neuron_404

7 days ago

x.com/i/article/2084…

0 1 29 33K 24

FIGURE 01 CAN RUN WITH A FLIGHT PHASE BETWEEN STEPS, PUTTING REAL-TIME BODY-MOTION PREDICTION AT THE CENTER OF THE DEMO The video shows Figure 01 shifting from a standstill into a fast running gait. It accelerates across an office floor, turns, and runs back past the furniture. That looks simple until you remember what running demands from a biped. Each step has a flight phase, so balance cannot wait for the next foot to land. The robot must predict its body motion before a bad step becomes a fall. Walking demos prove a machine can move through a controlled space. Running starts to test whether it can recover momentum in real time. That matters for warehouses, factories, and sites built around human walking speed. A robot that only walks slowly can assist. A robot that moves dynamically can begin to keep up with the work around it. The important detail is not the speed alone. It is the transition from standing, to running, to changing direction without stopping. This is still a clean indoor demo on a flat floor. Uneven ground, crowds, stairs, and long shifts are a much harder test. But the gap between humanoid robot clips and useful mobility is getting smaller. Bookmark this before you need the workflow. Follow for the next breakdown worth keeping.

boozie @soboozie

7 days ago

x.com/i/article/2084…

6 5 44 516K 45

FABLE 5 CAN TURN ONE POLISHED PRIVATE LINK INTO A $6K SITE SALE WHEN THE MOTION SYSTEM MAKES THE VALUE OBVIOUS Maya ran a two-person studio with strong concepts and weak portfolio pages. Her proposals felt premium, but her site still looked like a 2019 template. She used Fable 5 to build one polished landing page from a blank canvas. Then she animated the work instead of using a standard image gallery. Each section gave prospects a reason to keep moving toward the booking call. The service page worked like a small product demo before anyone replied. The first prospect asked if the motion system could fit their own brand. Maya sold the full site for $6K from that single private link. The reusable move is not copying a visual style or animating everything. Build one visual idea around the buyer's problem and make it interactive. Use animation to explain hierarchy, process, or proof at a glance. Fable 5 can speed up the first version, but taste decides the final result. When the concept is unclear, better transitions make confusion more expensive.

kocer @kocer_eth

a month ago

x.com/i/article/2074…

11 7 68 255K 88

@scrygg super interesting breakdown of treasury yields

@savipww saylor always comes up with crazy plays

@greednvirtue turning two bucks into 51k is insane discipline

@BLAZT_Ai seeing robots handle trade jobs is wild

@chopeaceus karpathy's wiki setup is such a smart approach

She's 21. Mara runs electrical installs for $4,100 a month, but the account built around that work brings in $7,300. Her audience is only 6,800 people. But they are local homeowners planning renovations, apprentice electricians, and site managers with an active problem to solve. They come for cable runs, panels, installs, and fault tracing because one clear answer can prevent an expensive mistake before they call someone out. That is why this audience is worth more to a supplier than a broad audience watching for entertainment. A tool brand is not buying random reach. It is buying access to people who can recognize the problem, trust the demonstration, and know which product they need next. Mara turns each ordinary job into repeatable media. AI sorts the raw footage into clips, drafts captions, schedules posts, and handles routine inbox and admin work. She spends about three hours a week reviewing the output, answering high-value questions, and recording the installs already on her calendar. Better local client inquiries: $2,500 a month. Supplier partnerships: $1,500 a month. Tool affiliate links: $1,200 a month. Electrical training products: $2,100 a month. Baseline work income: $4,100 a month. Account income: $7,300 a month. AI and tools: $49 a month. In month one, she posts enough installs to show the work is real. By months two and three, repeated questions reveal which faults, upgrades, and tool choices people will pay to understand. By months four through six, those useful clips become a library that keeps generating inquiries, partnerships, affiliate sales, and training-product demand. The install pays once. The media asset around the install can keep paying after the panel is closed. Bookmark this before the next AI account looks like luck. Follow for the system behind it.

kocer @kocer_eth

4 weeks ago

x.com/i/article/2069…

8 6 85 348K 140

WAYMO'S PASSENGER SCREEN CAN MAKE A DRIVERLESS RIDE LEGIBLE BY SHOWING THE ROUTE, DESTINATION, AND LIVE ARRIVAL TIME You open the door, buckle up, and press START RIDE on a passenger screen. There is no driver waiting to take over. The steering wheel turns by itself while the empty front seat stays empty. The screen shows the route, the destination, and a live arrival estimate. That is the real product change. A taxi ride becomes a software-guided trip instead of a service run by one driver. The passenger interface handles the small moments that normally need a person. It reminds you about your seat belt, offers support, and warns you not to forget bags. That matters because trust is built before the car moves, not only by its sensors. The ride in the video looks calm because every step is made legible to the passenger. But one smooth trip does not prove how the system handles every hard road situation. The useful test is simple: would you get in when the driver’s seat is empty? Bookmark this before you need the workflow. Follow for the next breakdown worth keeping.

hitu @hitu_monke

4 days ago

x.com/i/article/2085…

3 1 37 74K 33

AN KOREAN AI ENGINEER SHOWED ME THIS 4 FOLDER SETUP FOR COMPILING 1,000 FILES INTO MEMORY i used to dump entire pdfs and meeting transcripts into empty chat windows every single morning. the model would read them, give one useful response, and wipe its context clean as soon as the session ended. last week an ai engineer jumped on a private call and showed me their internal setup. instead of re-reading raw files, they compile every document into a 4-folder markdown architecture. raw documents act like source code. the model compiles them once into a structured wiki layer and reads from that layer forever. here is the 3-step operating system for permanent ai context: 1. raw/ (the immutable vault) › store every raw pdf, transcript, and note here without modifying a single character. › the model is strictly forbidden from editing raw files, keeping original source truth intact. 2. wiki/ & indexes/ (the compiled layer) › compile long documents into single-concept markdown pages inside wiki/. › indexes/ maintains a clean index.md navigation map so the model never guesses file locations. 3. instructions/ (the contradiction contract) › store prompt contracts that force the model to record conflicting data instead of overwriting history. › when a new source updates an old metric, both figures stay indexed with explicit timestamps. 90% of builders will keep feeding raw 70-page pdfs to standard chat defaults every morning. the 1% who compile their knowledge base once will run agents with permanent recall. save this 4 folder setup before you dump your next raw file into a chat window.

kocer @kocer_eth

4 days ago

x.com/i/article/2085…

3 0 20 29K 29

Most agent failures are not model failures. They happen when the agent retrieves the wrong context. CONTEXT ENGINEERING CAN GIVE AGENTS THE RIGHT INSTRUCTIONS, MEMORY, KNOWLEDGE, TOOLS, AND WORKFLOW STATE FOR THE NEXT ACTION A giant prompt cannot explain what matters in this exact moment. Context engineering starts with instructions and memory. It also needs knowledge, tools, and workflow state. Memory keeps durable facts from disappearing between tasks. Knowledge supplies the documents, code, and decisions for the job. Tools define what the agent can actually inspect or change. State records what has already happened in the workflow. Put stable rules in an agents.md, CLAUDE.md, or AGENTS.md file. Keep that file at the repository root. Then stop loading every document into every request. Knowledge graphs connect facts as nodes and relationships as edges. Score nodes by relevance and recency before adding them to context. The agent gets less noise and spends fewer tokens. It also gets a clearer path to the next action. Prompt writing still matters, but it cannot cover for lost context. Bookmark this before you need the workflow. Follow for the next breakdown worth keeping.

Diam @diamai_

5 days ago

x.com/i/article/2083…

4 2 73 50K 58

AN AI ANTROPIC RESEARCHER SHOWED ME THIS WORKFLOW DURING A PRIVATE GRAPH he did not upload 200-page pdfs into a fresh chat window every morning or maintain expensive vector embeddings. instead, he treated raw documents as immutable source code and compiled them into an obsidian markdown vault. the insider architecture pattern: › raw directory keep original pdfs, papers, and transcripts untouched so every claim can be audited back to the source. › compiled wiki let an llm compile raw files into small atomic concept pages with explicit bidirectional links. › index map maintain index.md with one-line summaries so agents navigate the vault without guessing file names. › contradiction log record conflicts between old notes and new sources rather than letting the model silently overwrite facts. when an agent needs an answer, it reads compiled concept pages instead of re-parsing 1,000 raw files from scratch. this simple change turned a noisy folder of disconnected notes into an active knowledge engine. inspect this workflow before uploading another raw document into a stateless chat window.

kocer @kocer_eth

4 days ago

x.com/i/article/2085…

3 0 20 29K 29

OBSIDIAN CAN GIVE A LOCAL HERMES AGENT A VISUAL AUDIT TRAIL THAT SHOWS HOW CLIENT NOTES, DECISIONS, ROLES, AND PROJECT CONTEXT CONNECT Most agents still start every run like it is their first day. That is a memory problem, not a context-window problem. Give the agent a vault it can read and update in Obsidian. Store daily briefs, role notes, decisions, project context, and logs. Each file remains a simple Markdown record a human can inspect. Now the agent does not need to carry the whole company in one prompt. It retrieves the relevant note, then writes down what changed today. That leaves usable context for the next agent or the next human. Obsidian's graph view is not the intelligence in this workflow. It is the audit trail behind roles, decisions, and missing context. One node can show how a client note connects to a later decision. Another can show which role owns a process before work gets lost. Start with daily briefs, agent profiles, project notes, and logs. Daily briefs record what happened, while profiles define responsibility and voice. Project notes hold durable decisions, and logs preserve work worth revisiting. A vault can preserve bad assumptions just as reliably as good ones. Use ownership, dates, and regular cleanup, or it organizes mistakes. The real win is traceable context, not AI that remembers everything. An agent should be able to show where its context came from. Bookmark this before you need the workflow. Follow for the next breakdown worth keeping.

Neheart @neheart

6 days ago

x.com/i/article/2084…

2 0 21 78K 31

@ScottyBeamIO real time VTubing setup goes crazy

@isssa0x need to find this video ASAP

LOOP ENGINEERING CAN GIVE EVERY REPORTED BUG A BOUNDED FIX ATTEMPT WHEN SUB AGENTS TRACE CAUSES BEFORE OPENING PRS Loop Engineering treats bug fixing as a scheduled process. Bug reports land in one database instead of one chat. Every 24 hours, a main agent reads the bug queue. It assigns a separate sub-agent to each reported bug. Each sub-agent first checks whether the bug is reproducible. Then it traces the likely cause and attempts a bounded fix. When there is something reviewable, it opens a pull request. The developer still decides what deserves to be merged. They review the diff and reject fixes built on weak assumptions. That is the shift most AI coding demos leave out. The value is not one impressive prompt in a chat window. The value is a repeatable route from report to reviewable PR. A loop keeps moving while the developer keeps control. An agent that opens a PR is useful. An agent that merges its own broken assumptions is expensive. Bookmark this before you need the workflow. Follow for the next breakdown worth keeping.

Aspect0 @Aspect0F

a week ago

x.com/i/article/2083…

4 2 24 43K 36

AMD HELIOS CAN TURN 72 MI455X GPUS, VENICE EPYC CPUS, AND DIRECT-TO-CHIP LIQUID COOLING INTO A 225 TO 245 KW AI RACK The visible copper blocks are not cosmetic. Helios uses direct-to-chip liquid cooling for a reported 225, 245 kW rack. That cooling budget is what makes the rest of the system possible. Each rack combines 72 Instinct MI455X GPUs with Venice EPYC CPUs. It also carries 31 TB of HBM4 memory for large-model workloads. The GPUs communicate through UALink networking over Ethernet. This is not simply a bigger cabinet packed with more accelerators. Large-model inference depends on data exchange and memory access. Headline compute matters less when those paths become the bottleneck. AMD is coupling GPUs, memory, interconnect, and cooling into one rack. The reported target is up to 30% more tokens per dollar than Nvidia Rubin. That is the number hyperscalers will watch when deploying inference. They will measure useful output against power, cooling, and hardware costs. The comparison still depends on the model, precision, workload, and final setup. Bookmark this before you need the workflow. Follow for the next breakdown worth keeping.

cesar ai @caesar_aii

a week ago

x.com/i/article/2083…

2 1 28 52K 25

@fingent_ai finally a realistic budget breakdown