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codex users, do this for astra or gpt-6 just point at it before it's too late [start prompt] Run an instruction debt audit of my agent setup. Find instructions that waste context, activate unnecessarily, contradict each other, cause premature stopping, or grant unclear authority. Preserve useful project knowledge and...

134,424 görüntüleme • 6 gün önce •via X (Twitter)

58 Yorum

Hussain Hashim | Building SundayBack profil fotoğrafı
Hussain Hashim | Building SundayBack6 gün önce

@Av1dlive I hit this wall hard myself. Realized half my rules were fighting each other. Cleaning it up was a game-changer for me.

Avid profil fotoğrafı
Avid6 gün önce

Your welcome brother

Hussain Hashim | Building SundayBack profil fotoğrafı
Hussain Hashim | Building SundayBack5 gün önce

@Av1dlive thanks, man! what's been your latest project?

Ziwen profil fotoğrafı
Ziwen5 gün önce

love this

Khairallah AL-Awady profil fotoğrafı
Khairallah AL-Awady6 gün önce

thank you for sharing this my man

Avid profil fotoğrafı
Avid6 gün önce

Your welcome brother

The Whizz AI profil fotoğrafı
The Whizz AI6 gün önce

valuable avid_:)

Avid profil fotoğrafı
Avid6 gün önce

thanks whizz

Jack Lipstone profil fotoğrafı
Jack Lipstone5 gün önce

instruction debt is a good name for it. most of ours turned out to be rules written for a bug that got fixed months earlier, still sitting there eating context. does your prompt separate the stale ones from the ones actively fighting each other?

Avid profil fotoğrafı
Avid5 gün önce

yes it does

twinedon profil fotoğrafı
twinedon6 gün önce

instruction debt is such a real thing lol, every agent setup ends up full of it

Avid profil fotoğrafı
Avid6 gün önce

Yes that’s so true

Oleks profil fotoğrafı
Oleks6 gün önce

instruction debt audits should be step zero before you add another system prompt

Roshni profil fotoğrafı
Roshni6 gün önce

Instant bookmark. Pruning prompt bloat is essential. 🔖

Avid profil fotoğrafı
Avid5 gün önce

Welcome Roshni, I hope you like it. Helped me a lot, it shall help you too.

Velo profil fotoğrafı
Velo6 gün önce

this is good one avid will give this to my agent

Alex Yarosh · AI expert · CEO of AI Studio profil fotoğrafı
Alex Yarosh · AI expert · CEO of AI Studio6 gün önce

Instruction debt audits should catch premature stopping before an agent drops the last required step.

Ruuj profil fotoğrafı
Ruuj6 gün önce

Good one avid, this makes things a lot more easier

Avid profil fotoğrafı
Avid6 gün önce

Thanks Ruuj I hope you like it

Gipp 🦅 profil fotoğrafı
Gipp 🦅6 gün önce

how do you batch audits for big repos?

Avid profil fotoğrafı
Avid6 gün önce

Pretty much the same way.

starmex profil fotoğrafı
starmex6 gün önce

alpha info avid, best!

Avid profil fotoğrafı
Avid6 gün önce

thanks brother i appreciate you

AH profil fotoğrafı
AH6 gün önce

Instruction debt is real; auditing before changing is the smart move

Avid profil fotoğrafı
Avid6 gün önce

yes its a must do

painn profil fotoğrafı
painn6 gün önce

i ll do this now

Avid profil fotoğrafı
Avid5 gün önce

Yes, painn. Thanks, brother.

volovuk profil fotoğrafı
volovuk5 gün önce

Audit first, don't modify" is the right instinct — just skim what it flags before deleting anything, since the model judging the rules is the same one that benefits from fewer of them.

leopardracer profil fotoğrafı
leopardracer6 gün önce

amazing share homie

Avid profil fotoğrafı
Avid6 gün önce

thanks leopard

Jamil profil fotoğrafı
Jamil6 gün önce

instruction debt audit before you add another skill. always-on rules are usually the mess

Winter profil fotoğrafı
Winter6 gün önce

instruction debt compounds when authority and completion rules conflict

Bradley Stone profil fotoğrafı
Bradley Stone5 gün önce

Is this similar to the '/doctor' command Anthropic cooked up?

Avid profil fotoğrafı
Avid5 gün önce

Not really

Bradley Stone profil fotoğrafı
Bradley Stone5 gün önce

Ah I see that one needs to be run manually in a terminal. There's also "consolidate-memory". They announced a couple more recently, too, I think.

Jurly profil fotoğrafı
Jurly6 gün önce

the scenario walkthroughs are strongest, since instruction conflicts usually surface only under real task pressure

Avid profil fotoğrafı
Avid5 gün önce

Yes, that is the precise reason why I put the video out.

Rich profil fotoğrafı
Rich6 gün önce

most agent setups probably need this badly

Avid profil fotoğrafı
Avid6 gün önce

Yes the audit worked wonders for me

GBE profil fotoğrafı
GBE6 gün önce

astra is sooo good

Avid profil fotoğrafı
Avid6 gün önce

It is good but people don’t use it well

Billy Luca profil fotoğrafı
Billy Luca5 gün önce

will try this

Avid profil fotoğrafı
Avid5 gün önce

How did you like the prompt?

Billy Luca profil fotoğrafı
Billy Luca5 gün önce

a well written prompt

Arbaz profil fotoğrafı
Arbaz6 gün önce

do this after every model bump. half my AGENTS.md was still written for a dumber model.

Avid profil fotoğrafı
Avid6 gün önce

Yes that’s spot on On top of that you need to carefully craft your agent.md

Arbaz profil fotoğrafı
Arbaz6 gün önce

yeah empty agents.md after the audit helped more than adding more rules.

‏عادل | مبرمج profil fotoğrafı
‏عادل | مبرمج6 gün önce

Great

Avid profil fotoğrafı
Avid6 gün önce

thanks brother

Roan profil fotoğrafı
Roan6 gün önce

thanks for sharing alpha

Avid profil fotoğrafı
Avid6 gün önce

Thanks roan

Max Bevza profil fotoğrafı
Max Bevza5 gün önce

need to run this audit right now

Dumb Programmer profil fotoğrafı
Dumb Programmer5 gün önce

Okay, let me try even though I am not a user of Codex

rewind profil fotoğrafı
rewind6 gün önce

interesting

Uriel profil fotoğrafı
Uriel5 gün önce

It's so structured that even Luna could do it. What's the point of doing it with Astra?

Saeed Anwar profil fotoğrafı
Saeed Anwar5 gün önce

Instruction debt is real and almost nobody audits for it. What's the most surprising conflict you found when you ran this?

Nokia04 profil fotoğrafı
Nokia045 gün önce

instruction debt 不还,agent 再强也是空转?

Tony Tong | Founder | Ancient Systems x AI profil fotoğrafı
Tony Tong | Founder | Ancient Systems x AI4 gün önce

Auditing your agent's instructions is smart, but the instruction that actually moved my business wasn't in a config file. Mine was a launch note: price at $29 for 25 keywords on the first 10 deals, then $49, cold email until someone pays.

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Avid

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Yarchi

58,549 görüntüleme • 3 ay önce

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Jerry Liu

22,763 görüntüleme • 20 gün önce

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Akshay 🚀

42,911 görüntüleme • 2 ay önce

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

Akshay 🚀

123,101 görüntüleme • 3 ay önce

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

194,171 görüntüleme • 5 ay önce

BREAKING: SpaceXAI has added a new guide called “Grok Bot 101” It explains how to create a personal AI teammate in just 10 to 15 minutes, teach it real workflows, connect apps, and build teams of specialized bots that continue working in the cloud even after you close your laptop. Here is the full guide in simple terms: • What is Grok Bot Grok Bot is an AI agent with its own persistent computer in the cloud. It has a desktop, files, terminal, browser, and apps. It can browse the web, use software, write and run code, and complete tasks just like someone using a computer. You can access the same computer from your phone or desktop. When needed, Grok Bot can hand control back to you for a CAPTCHA, 2FA, or secure login. • Creating a bot Give the bot a name, title, and detailed instructions. You can explain the workflow through chat or record yourself completing the task. Once trained, the bot can repeat that workflow whenever needed. It can also use MCP servers, plugins, skills, and connected services such as Gmail, Google Calendar, Google Drive, and Slack. • Three ways to use Grok Bot Send it a message in chat. Create schedules or triggers, such as monitoring a Slack thread or GitHub PR. Allow bots to message and activate other bots. • Permissions and safety You can write rules in normal language explaining what the bot can and cannot do. A separate review agent checks proposed actions and can allow them, block them, or ask you for approval. Allow and block lists provide additional control, while the work runs inside an isolated environment. One important detail: if you log into a website with one bot, other bots using that shared cloud computer can also access it. • Multi-bot teams You can create specialist bots for different jobs and let them work together. One bot can ask another for help, several bots can work inside a group chat, and a scheduled routine can move a task through multiple specialists until the work is finished. • Personal CRM The author created a bot that turned the 800 to 900 people he follows on X into a private Notion CRM containing public profile information. It helps him find and reconnect with people when traveling. • Arnold, the fitness bot He replaced a complicated fitness app with a Grok Bot strength-training coach named Arnold. The bot uses MCP servers, plugins, and skills while requiring less maintenance. Its behavior can be updated simply by chatting with it. • Building software Grok Bot gathers information from Slack, Notion, GitHub, documentation, and other sources. It then creates a clean prompt and sends it to a specialized Cursor cloud agent that builds the software. Grok Bot handles the planning and coordination, while the coding agent handles the actual build. • Searching company knowledge Grok Bot can search across codebases, Slack conversations, Notion pages, GitHub, and internal documents to answer product or company questions. This helps people find information faster and reduces the need to interrupt coworkers. • The biggest takeaway Instead of repeatedly building scripts or complicated apps, you can describe a workflow to Grok Bot, improve it through chat, reuse it, and let it continue working in the cloud. Grok Bot is not just another chatbot. It is a real AI teammate with a computer that can use tools, coordinate specialists, and get actual work done. The future of work is becoming incredibly exciting.

DogeDesigner

106,773 görüntüleme • 1 gün önce

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

377,138 görüntüleme • 5 ay önce

I realized that I would probably get more interest in my AI music theory tool (mtdt) and corresponding agent skill library if I did impressive things with more mainstream/popular tunes rather than an obscure Irish folk song from the 18th century like in my quoted post. So this time, I took my 3 favorite tunes from Zelda: Ocarina of Time (i.e., Saria's Song, the Song of Storms, and Zelda's Lullaby) and had them reimagined by the AI agent using my system in the style of various famous classical composers: Schubert, Schumann, Rachmaninoff, and Liszt. I had GPT-6 Astra xhigh acting as the "controller" agent create the prompt and launch another instance of Codex (the "worker" agent) in a fresh workspace with all 322 skills loaded, and then monitor the process. I gave the controller agent only very high-level instructions about which songs from the Zelda game I wanted and which composers to use for which songs. I also told it stuff like: "For actually rendering the written music to audio, we should use the nice concert piano instrument with reverb that came from my jazz chords project originally (see /cass) and we should humanize the playing with a skill so it doesn't sound robotic in tempo and dynamics; it should sound like a professional pianist specializing in the music of the particular composer. And try to do a final critical pass over each piece with fresh eyes looking for any deficiencies and blunders... does it REALLY sound like that composer's work? Can you still hear the original song's essence in the new, reimagined version? Is it good? Does it follow the rules and structure of music theory well, through the lens of that particular composer? If not, then FIX or IMPROVE those things (instruct the other agent to do so using its skills and the mtdt tool!)" --- But then the controller agent was in charge and handled managing the worker agent (which did nearly all of the actual labor of making the tracks). It's a bit counterintuitive that this creator-critic split would work better (why not just go directly to the agent doing the work?), but I sort of stumbled on it by accident because I didn't want to mess with my default agent skills and only wanted the 322 music theory skills to be visible to the worker agent. That's why I originally had the controller agent launch the other agent: to specify that. But then I realized that it's very handy to have an independent agent handling the details for me. It's sort of like a rich English aristocrat in the 1700s having an awesome butler to handle the rest of the household staff. Just tell the butler what you want in a kind of abbreviated shorthand and he will turn around and give very detailed instructions, monitor everything, and do quality control for you so you don't have to get into the nitty-gritty details so much. It gives you another set of eyes, even if it's using the exact same underlying LLM, and there's something about the gestalt shift of being a critic versus a creator that leads to better objectivity and honesty and, ultimately, better-quality results. Anyway, the first results made me realize that it had taken my instructions about being able to hear the original song clearly in the reimagined version too literally, so I told the controller agent: "It too directly has the entire original melody within it unaltered, which is jarring and not like . It's also too long; they should be closer to 1.5 to 2 minutes each. We don't need to start from scratch, but make sure you address those issues." That's about the extent of my feedback and iteration here. And to be clear, the original versions were pretty nice, too. I'll have some other fun demos coming out soon as well. Anyway, attached here are results from 4 composers as animated scores. You can also get the PDF of the complete scores here: So do they actually sound like those composers wrote them? It's sort of an impossible task, in that the style of the original melodies is really nothing like the styles of those composers. But within those constraints, I think it did a respectable job. If you're a fan of that Zelda music and like classical music you will probably get a kick out of these. I'm positive that with more focused prompting and iteration I could get much better results from the system, but I sort of just wanted to see what it could do with minimal interventions. Curious to hear what people think.

Jeffrey Emanuel

23,583 görüntüleme • 2 gün önce

This is a better and more challenging demonstration of my AI music theory cli tool and skills library. I told the agent only that I wanted to take the traditional Irish song, Clare's Dragoons, and to create a piano rendition of the main verse and chorus followed by a series of variations in the style of Mozart's famous "Twinkle Star Variations" (K. 265/300e), and that it should spawn Codex with the skills and prompt it to do that for me and to invoke any relevant skills in the system. This was in Codex with GPT-6 Astra xhigh and with the workspace loaded up with just the 320 skills that are part of the music AI skills library, and with the mtdt (music_theory_data_tool) cli tool available for use by the agent. The agent was able to find suitable sheet music for the Clare's Dragoons song and then used mtdt to convert that into its internal representation form, which makes it easy for the agent to work on the music and to convert it to any other desired format (like a PDF engraving, midi files, etc). It then researched the Twinkle Star variations and used various skills to compose and review and refine the variations. Why do I think this task is more demanding than writing a fugue in the style of Bach? Well, for one, it's a lot more structured, so you have much less flexibility and room for "artistic license." Ultimately, you have to render the original tune accurately, and then the variations have to sound similar enough to the original tune while introducing various creative musical manipulations to be interesting. It's pretty challenging to balance those two priorities: it's easy to lose the thread of the original tune, and easy to overcompensate for that and end up with something that only slightly tweaks one or two aspects of the original. Now, this is obviously not at the level of Mozart, but I think it's not that bad and will likely get much better over the next year as I improve the skills and tool and especially as models get better and more multimodal, so they can "listen" to the results in addition to reading the score. I'll provide the full prompt that the "controller" agent wrote for the agent that actually created the variations in a reply to this post. You can also see in the screenshots in this post the massive number of intermediate working files and artifacts created during the process, which gives a ton of visibility into how the agent did its work and reached its decisions. Not a black box! And I can assure you that I didn't mess with anything at all: this entire process was automated from start to finish and you're seeing the exact end product the agent produced without any revisions or feedback coming from me or any other human (the controller agent did ask for some revisions from the worker agent, which you can see in the final pic in this post). I'm trying to think of some other fun demonstrations. Maybe taking famous video game music (Mario 2? Ocarina of Time?) and making a version in the style of Schumann or Rachmaninoff. I'm open to suggestions!

Jeffrey Emanuel

11,607 görüntüleme • 2 gün önce

🚨 LIL DURK FEDERAL MURDER-FOR-HIRE TRIAL: KEY DETAILS FROM THE PROPOSED JURY INSTRUCTIONS 👀 The government and defense filed 82 pages of joint proposed jury instructions ahead of trial. The document lays out how Judge Michael W. Fitzgerald could instruct the jury on the charges, evidence, witness credibility, conspiracy, aiding and abetting, reasonable doubt and the deliberation process. Here are some of the biggest points: 1️⃣ THE CHARGES The proposed instructions identify five counts for the jury instructions: • Count 1 — Stalking conspiracy • Count 2 — Stalking of Tyquian Bowman • Count 3 — Stalking of Saviay’a Robinson resulting in death • Count 4 — Conspiracy to commit murder-for-hire • Count 5 — Murder-for-hire resulting in death ⚠️ The filing notes that the defense objected to beginning with Count Two, so the parties renumbered the counts for the jury instructions. 2️⃣ PRESUMPTION OF INNOCENCE The proposed instructions make clear that all three defendants have pleaded not guilty and are presumed innocent unless the government proves guilt beyond a reasonable doubt. The indictment itself is not evidence and does not prove the defendants committed anything. 3️⃣ THE GOVERNMENT HAS THE BURDEN The jury would be instructed that the government must prove every element of the charges beyond a reasonable doubt. The defendants do not have to prove their innocence or present evidence. 4️⃣ DURK DOESN’T HAVE TO TESTIFY If a defendant chooses not to testify, the jury cannot use that decision against him in any way. If a defendant does testify, the jury is instructed to evaluate his testimony the same way it evaluates testimony from any other witness. 5️⃣ WHAT COUNTS AS EVIDENCE The proposed instructions say the jury can consider: • Sworn testimony from witnesses • Exhibits admitted into evidence • Facts agreed upon by the parties But statements, arguments, questions and objections from attorneys are not evidence. 6️⃣ DIRECT VS. CIRCUMSTANTIAL EVIDENCE The jury would be instructed that both direct and circumstantial evidence can be used to prove a fact. The law does not automatically give one type more weight than the other. 7️⃣ WITNESS CREDIBILITY WILL BE IMPORTANT Jurors can consider several factors when deciding whether to believe a witness, including: • What the witness could actually see or hear • Their memory • Their behavior while testifying • Possible interest in the outcome • Bias or prejudice • Contradictory evidence • Whether their testimony makes sense alongside the rest of the evidence The instructions also say a witness doesn’t automatically become completely unbelievable just because portions of their testimony conflict with other evidence. 8️⃣ A WITNESS WHO DELIBERATELY LIES If jurors determine that a witness deliberately lied about something important, they may choose not to believe anything that witness said. But they can also believe certain portions of testimony while rejecting other portions. 9️⃣ JURORS CANNOT FOLLOW SOCIAL MEDIA This is a BIG one. The proposed instructions specifically tell jurors they cannot discuss the case on: 📱 Instagram 📱 Twitter/X 📱 YouTube 📱 Facebook 📱 TikTok 📱 Snapchat 📱 Blogs 📱 Text messages 📱 Email 📱 Internet forums They also cannot communicate about the case with family, employers, the media or people involved in the trial. 🔟 NO RESEARCH Jurors cannot independently research: • The case • The law • The defendants • Witnesses • Attorneys • People involved in the case • Locations discussed during trial They also cannot search the Internet for information about the case. 1️⃣1️⃣ JURORS ARE WARNED ABOUT OUTSIDE INFORMATION If a juror accidentally sees or hears something about the case outside the courtroom, they are instructed to report it to the court immediately. The proposed instructions warn that violating these restrictions could jeopardize the fairness of the proceedings and potentially result in a mistrial.

Cousin Tino ™️

28,422 görüntüleme • 8 gün önce

Y Combinator CEO, Garry Tan, took the stage for 42 minutes at Startup School 2026 and explained how to build your own personal AGI better than any paid AI course. This is what he told the room: 1. The leverage is in your context, not the model. Tan watches hundreds of founders use identical models every batch. "There are 2x people and there are 100x people who are using the same Claude. Same weights, same context window size, same API. But the leverage is not in the weights." The gap between users is now bigger than the gap between models. 2. One person's output went up 400x. In 2013 Tan shipped maybe 14 useful lines of code a day as a YC partner, dead on the median for programmer productivity. "I did the math on my output, and I'm at about 400x what I did in 2013." 3. Agents run on a different working memory. Humans hold 7 things in their head at once. Every org chart and checklist ever built is a patch for that limit. "An AI agent holds a million tokens. That's about a thousand pages. Three Harry Potter books sitting open on its head all at once." You're still running your week on tools built for the 7-digit brain. 4. Markdown is code now. Tan's stack is mostly skill files: pages of plain English an agent can execute. "If you can write clear instructions in English, you're a programmer. The compiler is a language model." At YC, finance and events staff who never opened a terminal are building automations. 5. Your history is your moat. Tan's agent runs on a personal wiki: about 220,000 markdown pages covering 25 years of email, meetings, notes and decisions. "When my agent does anything, it does knowing everything I know. And that's the difference between an assistant and a colleague." No frontier model has your context. That's the one asset nobody can replicate. 6. Never do one-off work. Most people run a task with an agent, close the window and throw the learning away. Tan ends every task by having the agent turn what it did into a reusable skill file. "If you have to ask for something twice, you failed." Captured skills compound daily. Amnesia resets you to zero every morning. 7. Own your skill files before your employer does. A skill file is your judgment, extracted and executable. The only question is who controls it. "Own your skills because if you don't, your job becomes a skill file." Files in your repo compound your career. Files in the company's repo run your judgment without you. Watch it, then read the step-by-step guide on becoming an AI engineer.

Alex Prompter

249,479 görüntüleme • 1 ay önce

2026 Fulton County Election Fraud Report finds THOUSANDS of FALSE BALLOTS added to the post-election hand count/audit, which lowered Biden's margin of victory and vote total by about 50,000 to 60,000 votes, indicating that Trump did indeed win Georgia in the 2020 election. This isn't even counting all of the other fraud found GA. As a result of the 36 errors, 6,691 fictitious ballots that do not exist were added to the "Total Ballots Cast" column. After removing the false ballots from the total, because they do not exist, Fulton County's corrected Total Ballots Cast for the hand count/audit is 521,341, or 7,436 ballots less than the certified Nov. 3rd total of 528,777. Of these, candidate Trump received 1,025 false votes that do not exist, while candidate Biden received 5,618 false votes that do not exist. Correcting for the errors from only the absentee ballots of one county, the hand count/audit results yield a margin of victory that is 4,593 votes less than the November 3rd results. 11,779 was the total margin of victory for the entire state. A review of only 3 percent of the ballots yielded errors that falsely inflated the margin of victory for the hand count/audit by one-third. Based on these errors alone, the margin of victory drops to 7,185. The 36 errors mysteriously added a sufficient number of ballots and votes to substantiate the November 3rd results, albeit falsely. Just as the hand count/audit was used as a metric to corroborate the November 3rd results, the errors call those results in to question. First, there was no investigation beyond that which was carried out by Mr. Rossi and the Governor's office as the Secretary of State's investigator did not perform an investigation. Second, the claim that the error were unintentional is refuted by the fact that 35 of the 36 inconsistencies benefited one candidate. Next, the failures were not the product of data entry errors. Lastly, the conclusion and excuse that the errors did not affect the outcome of the presidential contest is irrelevant and not responsive to the allegation, as the race for president extended past the Fulton County line, and so did the "errors" that were found. It gets even better... A total of eight 8 false batch entries are included in the results in which candidate Trump erroneously receives ZERO votes, and almost all are supported with a batch tally sheet. Errors with a batch tally sheet that don’t match the corresponding ballots are not the product of mistake or unintentional error. It is indisputable that the batch tally sheets identified by Mr. Rossi and Governor Kemp were intentionally fabricated to falsely pad the hand-count/audit results in line with the November 3rd results. Georgia law explicitly states that any superintendent or employee who intentionally destroys or alters tally papers, or permits them to be destroyed or altered, shall be guilty of a felony. Fulton County's chaotic, unaccountable curation and processing of cast ballots, cast BMD printout, and electronic records make a true risk-limiting audit impossible. It is unreasonable for voters to trust that their votes were counted at all, much less counted correctly. Voters have good reason to believe that some votes counted more than others, some votes were included twice or 3x in the totals. There is no way to know how many votes were omitted from the tabulation, absent access to the physical ballots and BMD printout and evidence that the chain of custody is intact. From the records produced so far, it Is impossible to determine whether malware, bugs, misconfiguration, or malfeasance disenfranchised voters or altered the election results. The fact that thousands of false ballots and votes were unveiled should have triggered a real investigation, not only of Fulton County's November 3rd election results, but those of the entire state. We can use the same mathematical basis as the Risk Limiting Audit by using Fulton County’s rate of error- and extrapolate. Approximately 148,000 absentee ballots were cast in Fulton County, and out of those, 6,691, 4.52%, were found to be false, or in error. A total of 1,311,061 absentee ballots were cast in the state of Georgia for the 2020 General Election. Using the same percentage, 4.52%, of false ballots/votes as that confirmed in Fulton County: 1,311,061 x .0452 = 59,259 false ballots/votes Using the same ratio of distribution of false ballots/votes as that confirmed in Fulton County: Candidate Biden: 83.9% of 59,259 = 49,718 false votes Candidate Trump: 15.3% of 59,259 = 9,066 false votes Therefore, using the Fulton County hand count/audit error rate, as established by the Governor's report on just the absentee ballots cast, the error rate is determinative, and it is possible that the wrong candidate did take office. Ms. McGowan's assertion that the hand count/audit errors did not affect the outcome of the race, is not supported by fact. Given the egregious manipulation in Fulton County, failing and/or refusing to check the hand count/audit results of the other 158 counties constitutes gross negligence, if not willful misconduct and fraud. On top of these findings, Fulton County election officials knew the hand-count/audit results were falsely inflated. There are a confirmed 6,691 fictitious votes that were added to the hand-count/audit results, but were NEVER corrected. An email found corresponds, which is located in this report, establishes the fact that Fulton County election officials knew of the errors at the time, even the same day that the Secretary of State released the results. (PAGE 171 in the video report above contains the email to view.) For some five 5+ years, the hand-count/audit results, known by Fulton County to be materially defective, have been used to falsely substantiate the official results. The same fraudulent results have also been used against those who rightfully questioned Georgia's election results. Lastly, for just this report and presentation, the hand-count tally sheets DO NOT MATCH results from advance voting polling locations. Poll tapes were compared for all tabulators at each polling location to the corresponding batch tally sheets as produced during the hand-count/audit. The fact that such differences between the hand counted audited ballot tallies and the official machine count tallies differs by this much signals that tabulation and auditing processes are flawed and strongly argue for intense objective expert examination and considerable mitigation efforts. It should be the case that such counts are consistent and exact. The fact that such audit discrepancies at a precinct level did not cause precertification investigations of the count variances is unacceptable, it essentially defeats the purpose of an audit if significant discrepancies are ignored and chalked up to human error. As they seem to have been at least in the case of the Fulton County audit. It is irrefutable that the hand count/audit results were indeed the result of intentional human acts, aka, FRAUD. Read full report for all details and visual evidence above in video report. The 2020 Election was stolen.

The SCIF

46,220 görüntüleme • 1 ay önce

How to set up Claude Cowork so it actually works like an AI chief of staff (not just another chatbot): 1. Most people open Cowork, type a message, and get generic output. It's not a Claude problem. It's a setup problem. Cowork needs context before it can help you. Who you are. How you work. What you're building. Your team. Your priorities. Give it that, and every session feels like picking up a conversation with an executive assistant. 2. The setup has three layers: a) Global instructions (who you are, how you work, what Claude should never do). b) Connectors (Slack, Gmail, Google Calendar, Notion) c) And a folder structure on your computer that acts as Claude's long-term memory. That combination is what takes it from generic to personalized. 3. Skills are the real leverage. A skill is a markdown file that tells Claude exactly how to do one thing well. Write my newsletter. Coach me on a decision. Review a case study. Each skill lives in its own folder with context, examples, and a definition of what success looks like. 4. We built a CEO coach skill in the video below. Gave it business context, leadership style, company goals. Then tested it with a real decision: should we increase our newsletter from once to twice a week? It came back with trade-offs, second-order consequences, and risk assessment. 5. Then we built a multi-agent advisory board. Five subagents, each with a defined persona: a) the operator b) the skeptic c) the customer advocate d) the finance partner e) the legal/risk advisor. You feed it a decision. Each agent evaluates independently. The main agent synthesizes the feedback. It's like having a board meeting on demand. 6. Third skill: a thought leadership content pipeline. Topic scoring, idea capture, distribution cadence, tone calibration. All built from your actual expertise and audience. Designed so an executive can go from idea to published post without starting from scratch every time. 7. The workspace map is what ties it all together. It's a top-level file that shows Claude how to navigate your entire setup. Which folders exist, what skills live where, how to invoke them. Without it, Claude has to search for everything. With it, Claude goes straight to what it needs. 8. Everything you build is portable. The folder structure works in Cowork, Claude Code, and Codex. Push it to a private GitHub repo and you can access it from your phone through Claude Code, or use Claude Dispatch. 9. The pattern is repeatable. Pick a task you do often. Create a folder. Build a skill. Add examples of what success looks like, and what a bad output looks like. Test it. Workshop it. Move on to the next one. Each skill is like onboarding a new employee who never forgets and never needs to be re-trained. The people who invest in this setup now are the ones who will have a 10x advantage when these tools get even better. And they're getting better fast. I sat down with Alex Lieberman on Human In The Loop and we built all three of these live from scratch. Full breakdown in the video below.. I tried to explain this as clear as possible for my non-developer crowd. Send it to someone who should be using Cowork but isn't yet. Or bookmark it to level up when you're ready. Watch 👇🏼

JJ Englert

573,446 görüntüleme • 5 ay önce

Loved this 22-minute talk on continual learning for AI agents. Must watch for anyone looking to get agents performant and into production. Credit: Soheil Feizi at AI Engineer • Agent learning can happen at three layers: the model (weights), the harness (prompts, tools, skills, code, workflows), and memory (session or persistent). • Two fundamental challenges: (1) getting feedback, meaning how do we know if the agent did well and what it should have done instead, and (2) acting on that feedback, meaning deciding which layer or component to change and how. • Feedback sources differ by stage: In development you have benchmarks with evaluators that score pass/fail. In production you only have logs, which can be judged either automatically (LLMs or code analyzing the log, which is scalable) or by human experts (low volume but critical domain knowledge). • Logs plus feedback aren't enough because they're not testable: A single log with feedback is one observation of what happened. You need to lift it into a replayable learning environment, a simulation with tools, users, and defined evaluators, so candidate fixes can be run, verified, and compared. • Three ways to optimize the agent, with tradeoffs: Model-layer updates (SFT, RL post-training like DPO/GRPO, LoRA) are expensive and need benchmarks and evaluators. Harness updates (trace-to-harness coding agents, prompt search like GEPA) are flexible but either untestable and "vibe-based" or benchmark-dependent. Memory updates (fact storage like Letta/Mem0, skill distillation) are cheapest and fastest but usually unverified. • A good learning engine makes "the smallest durable change at the right layer" of the agent. • Verifiable continual learning (VCL): Improve an agent from its own experience where every fix is proven to help and proven to break nothing that already worked. It requires an executable test (replayable failure), a measured delta (score before and after), and regression tests (prior tests still pass). • Four principles of practical VCL: Replayability (turn one-off failures into rerunnable tests), holisticness (one failure can have causes in memory, prompts, tools, workflow, or model, so route the fix to the right layer), lifelongness (fix new failures subject to no regression on past environments, with regression handled inside the optimization loop rather than post-hoc), and efficiency (the loop must run frequently and cheaply, without scaling linearly as past environments accumulate). • Three takeaways: (1) Agent continual learning isn't necessarily fine-tuning; many useful updates live in the harness and memory layers. (2) Production logs are not learning environments and must be transformed into replayable ones. (3) The frontier is regression-aware improvement: fixing new failures while verifying you don't break old ones.

Alex Lieberman

20,417 görüntüleme • 2 ay önce

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this

Yarchi

56,177 görüntüleme • 3 ay önce