Ironman_OBC

#wifeyfam

[OpenAI @OpenAI]

Codex Security Cloud is getting a major upgrade, with access to cyber-capable models through Daybreak Blue included by default.

It scans entire GitHub repos, continuously reviews new commits, investigates and deduplicates findings, and prepares fixes for review – even when your laptop is closed.

Available as a plugin in Codex desktop and web.

[Ironman_OBC @Ironman_OBC]

@batuhanppn 222

[Amisha Yadav @amisha_explains]

STOP WASTING HOURS TRYING TO FIGURE OUT WHAT TO LEARN IN AI.

I put together one practical roadmap with videos, GitHub repos, guides, books, research papers, and courses.

VIDEOS:
1. LLM Introduction — https://t.co/DilUVzd4KA
2. LLMs from Scratch — https://t.co/q0HSKUIwc6
3. Agentic AI Overview (Stanford) — https://t.co/eyLuA3k3mG
4. Building & Evaluating Agents — https://t.co/EYmiatzvlb
5. Building Effective Agents — https://t.co/k7QauZIBup
6. Building Agents with MCP — https://t.co/JeHUisjzm8
7. Building an Agent from Scratch — https://t.co/LBZyB7wVoy
8. Philo Agents — https://t.co/WL3Rfqv0sq

GITHUB REPOS:
1. GenAI Agents — https://t.co/tfUPjPalJz
2. Microsoft AI Agents for Beginners — https://t.co/z5AAk7O0Rs
3. Prompt Engineering Guide — https://t.co/uNyjnIklFB
4. Hands-On Large Language Models — https://t.co/bLCCamemAM
5. GenAI Agents — https://t.co/iBrLpJJ7Lo
6. Made with ML — https://t.co/ORmiE8Gjo4
7. Hands-On AI Engineering — https://t.co/8BkLqwd7uZ
8. Awesome Generative AI Guide — https://t.co/i0yzp1QO0o
9. Designing Machine Learning Systems — https://t.co/YCM5PsnNJ8
10. Machine Learning for Beginners — https://t.co/dwFLv0V8wt
11. LLM Course — https://t.co/yp9aup3OSL

GUIDES:
1. Google's Agent Whitepaper — https://t.co/3SzXLeTUuV
2. Google's Agent Companion — https://t.co/9ctzJoi5H5
3. Building Effective Agents by Anthropic — https://t.co/9g8exTxXND
4. Claude Code Agentic Coding Practices — https://t.co/gRYW3HXjOL
5. OpenAI's Practical Guide to Building Agents — https://t.co/0wszx4OeJM

BOOKS:
1. Understanding Deep Learning — https://t.co/V809a4HLmn
2. Building an LLM from Scratch — https://t.co/KaNjx4q3J3
3. The LLM Engineering Handbook — https://t.co/PH1U8Jtksf
4. AI Agents: The Definitive Guide — https://t.co/7NLjpAMsZg
5. Building Applications with AI Agents — https://t.co/0NLZCiqgfP
6. AI Agents with MCP — https://t.co/mUPjkTnXJb
7. AI Engineering — https://t.co/DiFJkzLbNs

RESEARCH PAPERS:
1. ReAct — https://t.co/cwkezXZiiv
2. Generative Agents — https://t.co/xj2rzRUVsa
3. Toolformer — https://t.co/N3QTcTEiH7
4. Chain-of-Thought Prompting — https://t.co/5c35Yetrv2

COURSES:
1. Hugging Face Agent Course — https://t.co/FY0k2INNLp
2. MCP with Anthropic — https://t.co/FIkFIvz5ar
3. Building Vector Databases with Pinecone — https://t.co/rLMLsXH1dZ
4. Vector Databases: Embeddings to Apps — https://t.co/gNpscSP5mx
5. Agent Memory — https://t.co/pbHPXpOUO2

No endless searching.
No information overload.
Just resources you can actually use. 🔖

Your future AI skill set will come from consistent building, experimenting, and learning.

Repost so someone else can find this roadmap, and pls consider following
@amisha_explains for more content around AI, Beauty, and businesses.

[shikamaru @shikamarutsai]

Sam Altman (CEO of OpenAI):

"Every night I have a few hundred agents running with GPT-6 Astra

You no longer need to write prompts, if you use GPT-6 Astra”

AI agents working while he sleeps, all monitored from his phone.

In 55 minutes, he reveals what you can build now - and what used to require an entire team.

Watch him break it down, then save the Astra-6 agents setup below

[Arti Shah @TechByArti]

Don't waste 2 years learning to become an AI agentic engineer in 2026.

Andrew Ng, the godfather of AI, gave the complete playbook to become one from scratch.

1 hour course. Free:

• 00:00 - AI agent basics
• 12:12 - AI Agentic workflows & design patterns
• 53:27 - Practical tips for building AI agents
• 1:20:30 - self-improving AI agent loops
• 1:30:19 - multi-agent AI systems

I watched it last night.

Halfway through, I realized I could get into Anthropic in weeks, not years.

Bookmark now. Watch it. Then build your own AI agent

[Ironman_OBC @Ironman_OBC]

@VolatilityQ Truth. Proud of what you have built. Keep it uo!

[Rahul @sairahul1]

This is what a one-person AI Agent run company looks like in 2026.

6 AI agents. 20 cron jobs. 0 human employees.

Every role is a folder. Every job description is a md file. No standups. No Slack. No payroll.

Just a directory on a Mac that runs the whole thing.

More on this below.

[The Icahnist @TheIcahnist]

The greatest fortunes in history were built by buying from those who had no choice but to sell.

These four men all became billionaires by following the same playbook: buying industrial assets that corporate giants wanted off their books.

Great books

[Yarchi @undefinedKi]

My second brain article passed 8 million views. So I put the whole setup, the tools, the learning material and everything around it in one place. Free

A repo and a site. What is in there:

> 109 pages on the site
> The guide: 10 sections and 65 pages, concept through troubleshooting
> 5 tracks on top of it, 44 pages, 15 of them build guides with code that runs
> 18 agent skills, 72 slash commands, 6 subagents
> 5 Python scripts: graph export, link checker, vault stats, chat converter, site builder
> A starter vault template with its own CLAUDE.md
> 87 resources: 28 tools, 26 Obsidian plugins, 15 repos, 12 skills, papers and articles

There is also a track for each of these:

> Knowledge graphs
> Jev engineering
> Agent harnesses
> Loop engineering
> Eval engineering

The Second Brain guide is a good starting point, and I'd recommend that beginners start there.

If you're a more advanced LLM user, move on to the additional resources. I'm sure you'll find a lot of useful information there.

The website and repository are constantly updated. New tooling shows up every week in this space.

Repo: https://t.co/GpYVL1Ogh5
Website: https://t.co/8dSeufUJAJ

[Rahul @sairahul1]

this is f*cking gold.

someone put the whole one-person company playbook on one page.

- pick one narrow problem.
- talk to ten buyers.
- write the case.
- publish one page.
- win the first customers by hand.
- automate what repeats.

AI handles research, content, sales prep, support and ops.
human (YOU) keeps the judgment.

that's the first solo fortunes of this era.

here's the exact guide to building the AI half ↓

save this and start today

[Ichigo @iiiichigo_chan]

Stanford AI engineering course:

“Anyone can build an AI agent in 60 minutes

The real skill is building the harness that makes it reliable”

Prompt → Agent → Harness → Revenue

Stanford just released a complete course on building AI agents from scratch:

00:00 – Build your first AI agent
48:17 – Create agents without coding
54:39 – Turn agents into a $100K+ business

While you scroll, someone else is learning Anthropic’s $750,000 skill set

This free course is better than most paid AI agent programs

Bookmark it and watch it today

Then read the article below to learn how to build the harness around your agents

[Superpower @superpower]

There's a peptide in your blood that drops by more than half by age 60.

It's tied to collagen, hair growth, and wound healing.

In trials, restoring it closed diabetic wounds 3x faster and improved wrinkles, firmness and skin density in 12 weeks.

Here's the breakdown: (1/17)

[Zephyr @Zephyr_hg]

this is f*cking gold

15 GitHub projects, 1.21 million stars between them, and together they're a complete AI employee.

Specs. Memory. Web data. Documents. Context. Sandboxes. Monitoring. Video. The loop is: define the job, collect the evidence, parse the docs, save the memory, run the code safely, watch what changes, ship.

All open source. All free. The people building one-person companies aren't buying tools, they're wiring these together. Save this, then read how they wire it ↓

[0xRafy @0xRafy]

SpaceXAI just dropped a 12-page PDF on best GrokBot use cases - after reading you will use GrokBot better than 95% of people

What Grok Bot is, which use cases work today and how to deploy without burning trust in my 12-page research:

step 1 → every working use case follows one shape: read, check, apply rule, write draft. If your task fits this, the bot can handle it

step 2 → five criteria before handoff: repeatable, cross-system, rule-governed, draft-first, bounded error cost. Fails on any? Add a gate or wait

step 3 → start with sales. 36 LinkedIn drafts queued overnight. Zero sent without review. Highest density of agent-ready work

step 4 → engineering gets bug repro, CI/CD monitoring and dependency audits. Bot catches the ticket, hits staging, drops a repro pack

step 5 → operations runs inbox, expenses and travel. Bot signs into portals without APIs. Finance saves 8-12 hours at month-end

step 6 → marketing owns 13 of 56 roles. Paid media, content calendars, competitor watch. All overnight, all draft-first

step 7 → the approval boundary: every official use case stops before external action. Nothing sends, books or pays without you

step 8 → deploy in four weeks. Watch, review all, review flags, scheduled routine. Skip a week and risk compounds

step 9 → fleet pattern: Chief of Staff on top, specialists underneath, bots message each other. You stop being the middleman

step 10 → sort by cost of mistake. The bot nails 49 tasks then does something odd on the 50th with full confidence. Keep a human at every exit

the result: 56 roles, one playbook. The bot handles the 80% that repeats. You handle the 20% that matters.

this 12-page PDF learns you how to get best results from GrokBot

save now, then read how to build a 24/7 GrokBot agent team in the article below

[Carbonyx @carbonyxxx]

hedge funds pay poker players $400,000 a year - not finance grads, not MBAs - because the casino table and Wall Street run on the exact same math, and the poker player already knows how to read it.

Kevin Desmond - poker pro, instructor of MIT's Poker Theory and Analytics course.

the line that should stop you cold: "basically all of the value you're losing is from screwing up pre-flop."

read that again for your own money. you do not lose the hand in the moment - you lose it before you ever act, in the decision to be in it at all.

it is the exact mistake that bleeds traders dry. they obsess over the exit - while every dollar was won or lost the second they chose to enter.

Desmond's framework builds to one goal: reach the level where no pro at the table wants to sit across from you. the market runs the same test on every position you open.

most players only get good enough to beat bad players, then stall for life - never seeing the edge that lives purely in the math, invisible to anyone playing on feel.

the course is free, hand-analyzed, MIT-grade. the game was never the cards - it was the discipline to fold before the money is in. almost nobody who bleeds money to the market has opened it.

[DogeDesigner @cb_doge]

Here’s how to check if your 𝕏 account is shadowbanned or has any visibility labels limiting the reach of your account or posts:

1) Open https://t.co/271Oc9GcSc
2) Click “Download”
3) Open the downloaded JSON file in any browser or text editor

How to read the report:

• period: The dates covered by the report
• generatedAt: When the report was created
• postCount: Number of posts analyzed during that period
• postLabels: Visibility labels applied to individual posts
• accountLabels: Visibility labels applied to your account
• totalPostLabels: Total post-level labels
• totalAccountLabels: Total account-level labels

An empty bracket [] means no labels are listed. If both totals show 0, no post or account visibility labels were recorded during that reporting period.

Note: If the link is not working for you, the feature is still rolling out to more eligible users. You can check again later.

𝕏 lets users inspect this information instead of leaving them guessing. This is real transparency.

[Noisy @noisyb0y1]

She's 18 built an AI agent with Opus 5 and sold it to Anthropic for $3.2M - and came to Stanford to show how to do it from scratch:

00:34 - how Opus 5 builds a $3.2M agent in one evening
15:34 - 4 agents replaced 400 Anthropic engineers
34:47 - from first prompt to a $3.2M check from Anthropic

after watching I spent 60 minutes building my first agent - it cut my workday by 90% and a week later I got a $100k check from Anthropic:

save & watch - article below on how to go from one prompt in Claude Code to an agent people pay millions for.

[Eleanor @HeyEleanorr]

Dementia is blood flow.
Dementia is cholesterol.
Dementia is preventable close to half the time.

8 simple rules to protect your brain:

1. Floss your teeth

[Arvind Srinivas @glocalinvestor]

Goldman's reading on Photonics:

Here's 10 Key takeaways from their visit to optical companies during SemiCon, China AI tour and Asia leader conference.

Goldman also shared their "stock list with price target" which you can find it at the bottom.

Optical Module demand outlook
- AI server ramp up, module adoption expansion & spec upgrades are key demand drivers
- Global: 800G & 1.6T demand upto 130-200M
- China: ~50M mainly in 400G & 800G, 1.6T emerging.
- Supply crunch until 2027.

Competition
- Healthy pricing competition with 800G waning
- 1.6T price to sustain ~$700+
- Product cycle to come down to 1-2 yrs in data communication vs 5-6 yrs in telecom
- Tight supply situation exacerbated by customers' aim to diversify to avoid geopolitical backlash
- Innolight has the leadership through R&D, suppler mgmt and LTAs

Laser Supply & Spec upgrade
- Chinese players are ramping up capacity and capability in 200G EML lasers
- InP supply improving due to steady export permission from Chinese govt.
- As transition from scale out to scale up is underway, FAU and laser specs are also undergoing upgrades.
- FOCI's FAU in 2026: 40 channels for 3.2T; 80 channels for 6.4T and 100 channels are under R&D.
- FOCI customers: $NVDA $TSM $AVGO $AMD $COHR $LITE $GLW to name a few.
- VPEC growing on the back of CW lasers & InP demand

Spending sustainability
- Players are widely expecting the capex cycle to last 10-20 years. Some already planning beyond 2030
- Demand for upcoming 2-5 years very strong
- Capex includes: Data centers, technology improvement, growing supply chain
- Vendors see much more transparency from end customers compared to dot-com bubble era.

Testing
- CPO has its challenges in commercialization
- GS believes testing equipment providers would be the earliest beneficiaries of CPO
- Companies not only need more but also better testing tools
- Robotechnik/FiconTEC, global PIC leaders, aim to shorten wafer level testing time by 50-60% next year
- They are also targeting 4x faster die-level testing
- Check out @LIWEI_TWCapital who has been covering the testing challenge in CPO extensively.

GS's Stock list with 12-months PT:
- Zhongji innolight. RMB 2645
- VPEC. NT $793
- RoboTechnik. RMB 796
- ASMPT. HK$77