@navalnewscom
AIで完全放置させているサイトのSEO改善プロンプト スキル名: SEO Rank Watch 対象サイトのGoogle検索順位を継続的に改善する。 目的：1位を取れそうなキーワードを見つけ、検索ニーズに答える改善を1つ行い、7日間観察する。これを1位になるまで繰り返す。 データ data/seo/watchwords.json — キーワード / 対象ページ / 優先度 data/seo/rank-history.json — 順位履歴。追記専用 data/seo/improvement-log.json — 改善履歴 / status / 次回レビュー日 status： active — 改善候補 observing — 改善後7日間の観察中 achieved — 1位達成。監視のみ Workflow 1. 順位を測る 原則としてGoogle Search Consoleを使用する。 node.claude/skills/seo-rank-watch/scripts/fetch_gsc_ranks.mjs \ --repo <REPO_PATH> --append GSCの平均…
人生で初めてAIが面接に来た動画を公開します。 注意喚起も込めて。 補足すると動画スタートが面談スタートではなく、なんか変だなって思っててCOLOさん？（KOWROや）ってなったあたりから頬杖ついてAIだと確信を持ってます笑 絶賛「人間」の採用を加速しております。ぜひご応募ください↓↓↓
10 GitHub repos that seem "illegal" but are perfectly legal 1. yt-dlp → https://github.com/yt-dlp/yt-dlp Downloads videos from any platform. YouTube Premium charges 16 dollars a month to do less 2. Ollama → https://github.com/ollama/ollama Runs AI models on your laptop without paying for APIs. What costs many hundreds a month here comes out to 0 3. Fooocus → https://github.com/lllyasviel/Fooocus Generates images with Midjourney-quality on your GPU. Midjourney charges 30 dollars. This is unlimit…
🚨只改一个文件，能让GPT-6 Astra Token直接少烧35%+！ GPT-6 Astra不是贵，是你的AGENTS.md太肥了 省Token只做一件事：把仓库根目录那份 AGENTS.md 换成精简版（如图） 修改后实测数据很狠： 🔹普通改代码省 5%–15% 🔹跨文件长任务省 10%–25% 🔹爱反复搜索、重复验证的任务能到 30%–35%+ GPT-6 Astra 协作规则真正砍Token的不是文字变短，是把Agent的坏习惯掐死： 🔸先给结论，不复读需求 🔸已确认的信息不重读、不重搜 🔸证据够了就停，找到原因就修 🔸已授权操作不反复确认 🔸验证力度跟这次风险对齐，不为“看起来严谨”加测试 🔸交付物出来、必要验证过、限制说清，立刻收工 🔸子Agent只在独立且收益大于协调成本时才拆 GPT-6 Astra对 AGENTS.md 更敏感。文件越全，它越容易被旧规则带着空转。 根文件只留稳定规则，细节按需加载，账单才会掉。 收藏对照改。改完拿你最费Token的那个长任务对比一次，很多时候省下的不是35%，是整段无效探索。 #GPT6 #Astra #AG…
【話題】“話し合い”ができない夫を激詰めした結果、離婚を切り出された女性が話題に ・夫と議論しようとすると「明日にして」と逃げられる ・夫は“話し合いができない人”で、大体何でも夫が悪いというスタンス →既婚男性から「読んでてツラい」「うちの嫁が書いた文章かと思った…」との感想が相次ぐ
VM？
Wait for Grandma 🤯 if you are married, I advise you not to open the commentsss
個人的に無料版CatGPT,Claudeでも一発で読み込めるadhx.com使ってるな〜 𝕏.comの先端に三文字追加するだけでいいし… (というかfxtwitter何故か読み込めなかった)
@nostalgiafkninc
BREAKING: Liberty caps breach the Anglo-Scottish border 🍄
Stay safely away from these mushrooms. Especially if you’re struggling with mental health, or looking to laugh with friends.
2w
Morning stroll to the beach
Me explaining AI to friends
What’s the point of 4K?
Honestly, most people use 4K for those hyper-detailed, slow-mo macro shots. And I get it—they work because they grab attention. But I’m interested in something else: a raw, realistic aesthetic that feels less like a digital file and more like a real film—the kind that forces you to stop and watch, rather than just scroll past.
When @capcutapp invited me to try Seedance 2.0 4K, I didn't want to make just another promo ad. I wanted to see if I could push the tech into territory that feels like a real film.
I set up a few stress tests to see if this 4K could handle cinematic storytelling:
- Dark Environments: Low-light gradients that usually turn to digital mush at lower resolutions.
- Vibrant Color: Hyper-saturated schemes that often band and block.
- Macro Shots: Using those "attention-grabbing" angles, but giving them a narrative purpose rather than just using them as a gimmick.
I channeled the gritty, flashing-light aesthetic of Gaspar Noé for this. (A quick warning: his films are intense—careful.)
The result is "Salty."
Seedance 2.0 4K handled the mood and the detail better than I expected. Whether you're making films for fun or for a living, it’s a powerful tool for bringing those wilder, cinematic ideas to life.
Huge thanks to the team at CapCut for trusting me with early access to Seedance 2.0 4K. Getting to experiment within CapCut Video Studio was a true honor—I really appreciate the creative freedom they gave me for this film.
Salty
A film by Jordan Daniel Chesney
Produced by CapCut
Music by Brendon Moeller
@capcutapp #capcut
4K YouTube version in the comments.
I stole this idea and now use it with every single employee.
It’s the best illustration I’ve seen of teaching someone to be high agency.
It says there are 5 levels of work:
Level 1: “There is a problem.”
Level 2: “There is a problem, and I’ve found some causes.”
Level 3: “Here’s the problem, here are some possible causes, and here are some possible solutions.”
Level 4: “Here’s the problem, here’s what I think caused it, here are some possible solutions, and here’s the one I think we should pick.”
Level 5: “I identified a problem, figured out what caused it, researched how to fix it, and I fixed it. Just wanted to keep you in the loop.”
Using this framework, here’s what I say to every new employee…
You will live at Level 4 from Day 1 and as we build trust you will rise to Level 5.
Being high agency doesn’t just mean tackling problems in this way. It means your entire way of working should be oriented to being a Level 4+ employee.
Plz feel free to steal it as well.
And ty @stephsmithio for the framework!
Because this has gotten some recent attention again.
Apex Legends has bugged FPS ranges, like 140-160FPS for example, where you are severely magnetized to the floor when trying to Slidehop.
This Issue has existed for it's entire Lifetime and has been raised to Respawn multiple times.
That this still hasn't been addressed in a self proclaimed Movement shooter is completely unbelievable. So i'm once again calling public attention to it in yet another attempt to get this actually looked at.
If you regularly reach the following FPS Ranges you HAVE TO Cap your FPS below them to prevent this magnetization issue.
- 65-85FPS
- 140-160FPS
- 205-230FPS
- 275-300FPS
Even very shallow inclines extend these ranges even further.
If you regularly reach FPS in these ranges use the +fps_max command to cap your FPS below one of these ranges. For example “+fps_max 270”.
To be safe around inclines set your fps cap 5-10FPS below these ranges.
I can guarantee you there will be people under this post utterly flabbergasted that this was the issue all along for why their movement felt so much shittier compared to the smooth movement of the Content creators they watch.
Andrej Karpathy built the most watched AI repo of the year in one month, wrote most of it by hand, and just explained why he rejected vibe coding
he used autocomplete, not agents
his reasoning: LLMs have too many cognitive deficits for code that has never been written before
they kept trying to force him into PyTorch's DDP container, he had a custom gradient sync implementation, the models couldn't internalize that
they kept bloating his code with try-catch statements, kept using deprecated APIs, kept trying to turn research code into a production codebase
he said typing English instructions is slower than just navigating to the right line and letting autocomplete finish it
3 tiers of how people interact with code right now:
> reject all LLMs and write from scratch, probably wrong
> use autocomplete but stay the architect, his sweet spot
> full vibe coding with agents, works for boilerplate, breaks on anything novel
he vibe-coded two things: a boilerplate report generator and a Rust tokenizer rewrite where he had Python tests to verify against
everything else was hand-written with autocomplete
the part that matters for AI timelines: the main story about AI exploding to superintelligence depends on AI automating AI research, Karpathy says that's exactly what models are worst at
they know things, they don't fully know how to integrate them into your repo, your style, your assumptions
his current oracle: GPT-5 Pro, copy-paste the entire repo, ask questions, often surprisingly good compared to a year ago
but his verdict: the industry is making too big of a jump, it's slop, they're not coming to terms with it
Bookmark & Watch, then decide where you sit on the autonomy slider
You have an old Android phone in a drawer right now. Collecting dust. Worth nothing.
Someone built a script that turns it into a full Linux desktop. Or a smart home server. Or a development machine. For free.
It's called linux-android.
One script. No root required. No flashing. No risk of bricking your device. Run it in Termux and your old phone becomes a Linux computer.
Here's what it installs:
→ Full Linux desktop. XFCE4, LXQt, or MATE. Real windowed desktop on your phone. Connect a monitor and keyboard via USB and it looks like a PC.
→ Smart home server. Home Assistant runs on your phone. Control your WiFi lights, plugs, and smart devices from any browser on your network. No cloud needed.
→ GPU acceleration. Snapdragon phones get near-native GPU performance through Turnip Vulkan drivers. Mali GPUs use software fallback.
→ SSH server. Access your phone from any computer on your WiFi. Full terminal. Transfer files. Write code. All from your laptop keyboard.
→ Wine support. Run basic Windows applications on your Android phone through Box64 translation.
→ Audio support. PulseAudio configured automatically.
→ Works on any Android phone with Termux support.
Here's the wildest part:
A Raspberry Pi 4 costs $35 to $75. A used mini PC costs $100+. A VPS costs $5/month forever.
That old phone in your drawer? It has a faster processor, more RAM, a built-in battery backup, WiFi, and a touchscreen. All for $0. You already own it.
A Snapdragon 855 from a 2019 phone still outperforms most entry-level server chips. You're throwing away a computer every time you upgrade your phone.
Not anymore.
One command. One old phone. A full Linux machine.
100% Open Source. MIT License.
Happy Halloween! 🎃
Voice: @saruei
Voice/Sfx: @MimiHung_VO
For full transparency, here’s my track record over the last 6 months:
I publicly called the exact Bitcoin top at $126K.
I shorted ZEC at $717 (which ended up being the exact top again).
I told you to buy OIH and XLE, both are up 12%+ in just a few days, but this is a multi-year trade.
I bought NTR a few days ago (currently at breakeven). I’m still accumulating, but this is a three-year trade anyway.
I don’t make a lot of calls because, in my opinion, good setups are usually rare.
But when I make a move, you at least know my conviction is through the roof.
My goal here is to become the first person on this app to reach 1M followers without making $1 from my audience.
all while delivering as much knowledge as I possibly can.
$10k, $100k, $1M, I don’t care. I’m not interested in promoting stuff.
I don’t need money. Thank you.
My tweets are very time-sensitive (the market moves fast). Turn on notifications so you don’t miss anything.
Big things are coming this year. Let’s keep fighting Wall Street and win together 🤜🤛
So many people hating on how many lines of code this is and how it's "slop"
But Claude Code still has the best memory, context recall and speed that all agent builders should learn from.
1. Loads memory from 6 priority layers, bottom to top:
Org policy → user prefs → project rules → local overrides → auto-extracted memories → team-synced shared memory.
Rules can be conditional: they only activate when you're touching matching files. Auto-recall uses a side-query to rank and pick the top 5 most relevant memories from 200+ candidates.
The whole thing is memoized per session. It's closer to a filesystem than a prompt.
2. Compresses Context with 3 tiers:
Tier 1: Replay a pre-extracted session summary ((min 10K tokens, max 40K). No API call. Fastest.
Tier 2: Surgically prune old tool results from the cached prefix without rebuilding the cache. Cache-safe.
Tier 3: Full conversation summarization via Sonnet. Last resort. Has a retry loop that progressively truncates the head if the compaction request itself is too long
3. Parallelization everywhere
Startup fires keychain reads, git ops, and subprocess work all concurrently: I/O finishes during module import, effectively free (65ms). Nothing blocks first render.
File search returns results in ~5ms before the index finishes building. Fork agents share byte-identical request prefixes for max prompt cache reuse.
Tools declare themselves as concurrency-safe and execute in parallel during streaming. If one errors, siblings abort but the parent query continues.
A function literally named DANGEROUS_uncachedSystemPromptSection() exists to scare devs away from breaking the cache.
Nothing is allowed to be slow.
you might have one of the most underrated baseline performance optimizers in your medicine cabinet right now.
aspirin.
a few cents per dose, and it's doing things that many $$$$ supplements foolishly try (and fail) to emulate.
dr. ray peat spent decades on this. here's what he found.
but let's get this out of the way; it's not a pain drug. it's a metabolic drug.
aspirin stimulates mitochondrial respiration; the process by which your cells actually generate energy.
it activates both glycolysis and mitochondrial function simultaneously, shifting your metabolism toward efficient oxygen use.
this mirrors the actions of thyroid hormone.
it's pro-thermogenic. it counters the hypothermic, low-energy state that chronic stress, aging and modern life push you into.
the anti-fever reputation is misleading.
aspirin doesn't suppress metabolism; it enhances mitochondrial oxygen consumption while reducing pathological inflammation.
those are 2 different things.
and the anti-inflammatory picture goes deeper than most people think.
looksmaxxers, lock in.
by inhibiting COX enzymes, aspirin lowers prostaglandins and free fatty acid release; two of the primary drivers of puffiness, water retention, and tissue breakdown.
it protects against lipid peroxidation, guards DNA and proteins from free radical damage, AND it reduces oxidative stress linked to heart disease and neurodegeneration.
it's also anti-estrogenic.
it blunts aromatase activity and has mild anti-prolactin effects. it lowers cortisol's visible impact on tissue.
for anyone thinking about their hormonal environment, aspirin could quietly tilt it toward regeneration.
epidemiological data consistently shows reduced risk of breast, colon, and prostate cancer with regular low-dose use.
it inhibits abnormal cell division while leaving normal cellular growth intact.
neurologically, it enhances mitochondrial energy production in the brain, protects against excitotoxicity, and inhibits prostaglandin sy…
how to use obsidian + claude code to build a 24/7 personal operating system and build your startup:
1. write everything in markdown (daily notes, projects, beliefs, people, meetings)
2. link your notes together so they mirror how your brain actually thinks.
3. install obsidian cli so claude code can read your entire vault + the relationships.
4. stop reexplaining projects every session. use reference files instead.
5. build custom slash commands:
/context → load your full life + work state
/trace → see how an idea evolved over months
/connect → bridge two domains you’ve been circling
/ideas → generate startup ideas from your vault
/graduate → promote daily thoughts into real assets
6. keep a strict rule: human writes the vault. agents read it, suggest, execute.
7. let claude aka clode surface patterns you’ve been unconsciously circling for years.
8. delegate from inside your notes. one sentence in obsidian → agent handles the rest.
9. treat writing as leverage.the more you write, the more context your agents have.
10. understand this:markdown files are the oxygen of llms.
i really enjoyed seeing how to use obsidian thanks to @internetvin
vin uses ai like a thinking partner wired into his life’s work.
99.99% of people won’t do this because it requires reflection + setup.
but once the vault exists, the agent stops being generic.
it starts thinking in your voice.
episode is live on @startupideaspod (more there)
this one is different. send this tweet to a friend.
im still processing how game changer obsidian + claude code is, maybe you too
watch
BREAKING:
Proof—a new product from @every
It’s a live collaborative document editor where humans and AI agents work together in the same doc. It's fast, free, and open source—available now at.
It’s built from the ground up for the kinds of documents agents are increasingly writing: bug reports, PRDs, implementation plans, research briefs, copy audits, strategy docs, memos, and proposals.
Why Proof?
When everyone on your team is working with agents, there's suddenly a ton of AI-generated text flying around—planning docs, strategy memos, session recaps. But the current process for collaborating and iterating on agent-generated writing is…weirdly primitive.
It mostly takes place in Markdown files on your laptop, which makes it reminiscent of document editing in 1999.
Proof lets you leave.md files behind.
What makes Proof different?
- Proof is agent-native: Anything you can do in Proof, your agent can do just as easily.
- Proof tracks provenance: A colored rail on the left side of every document tracks who wrote what. Green means human, Purple means AI.
- Proof is login-free and open source: This is because we want Proof to be your agent's favorite document editor.
Check it out now, for free—no login required:
📂 SaaS
┃
┣ 📂 Idea
┃ ┣ 📂 Problem Discovery
┃ ┣ 📂 Market Research
┃ ┣ 📂 Niche Selection
┃ ┣ 📂 Competitor Analysis
┃ ┗ 📂 Opportunity Mapping
┃
┣ 📂 Validation
┃ ┣ 📂 Customer Interviews
┃ ┣ 📂 Landing Page Test
┃ ┣ 📂 Waitlist
┃ ┣ 📂 Pre Sales
┃ ┗ 📂 Demand Testing
┃
┣ 📂 Planning
┃ ┣ 📂 Product Roadmap
┃ ┣ 📂 Feature Prioritization
┃ ┣ 📂 MVP Scope
┃ ┣ 📂 Tech Stack
┃ ┗ 📂 Development Plan
┃
┣ 📂 Design
┃ ┣ 📂 Wireframes
┃ ┣ 📂 UI Design
┃ ┣ 📂 UX Flows
┃ ┣ 📂 Prototype
┃ ┗ 📂 Design System
┃
┣ 📂 Development
┃ ┣ 📂 Frontend
┃ ┣ 📂 Backend
┃ ┣ 📂 APIs
┃ ┣ 📂 Database
┃ ┣ 📂 Authentication
┃ ┗ 📂 Integrations
┃
┣ 📂 Infrastructure
┃ ┣ 📂 Cloud Hosting
┃ ┣ 📂 DevOps
┃ ┣ 📂 CI CD
┃ ┣ 📂 Monitoring
┃ ┗ 📂 Security
┃
┣ 📂 Testing
┃ ┣ 📂 Unit Testing
┃ ┣ 📂 Integration Testing
┃ ┣ 📂 Bug Fixing
┃ ┣ 📂 Performance Testing
┃ ┗ 📂 Beta Testing
┃
┣ 📂 Launch
┃ ┣ 📂 Landing Page
┃ ┣ 📂 Product Hunt
┃ ┣ 📂 Beta Users
┃ ┣ 📂 Early Adopters
┃ ┗ 📂 Public Release
┃
┣ 📂 Acquisition
┃ ┣ 📂 SEO Wins
┃ ┣ 📂 Content Marketing
┃ ┣ 📂 Social Media
┃ ┣ 📂 Cold Email
┃ ┣ 📂 Influencer Outreach
┃ ┗ 📂 Affiliate Marketing
┃
┣ 📂 Distribution
┃ ┣ 📂 Directories
┃ ┣ 📂 SaaS Marketplaces
┃ ┣ 📂 Communities
┃ ┣ 📂 Partnerships
┃ ┗ 📂 Integrations
┃
┣ 📂 Conversion
┃ ┣ 📂 Sales Funnel
┃ ┣ 📂 Free Trial
┃ ┣ 📂 Freemium Model
┃ ┣ 📂 Pricing Strategy
┃ ┗ 📂 Checkout Optimization
┃
┣ 📂 Revenue
┃ ┣ 📂 Subscriptions
┃ ┣ 📂 Upsells
┃ ┣ 📂 Add-ons
┃ ┣ 📂 Annual Plans
┃ ┗ 📂 Enterprise Deals
┃
┣ 📂 Analytics
┃ ┣ 📂 User Tracking
┃ ┣ 📂 Funnel Analysis
┃ ┣ 📂 Cohort Analysis
┃ ┣ 📂 KPI Dashboard
┃ ┗ 📂 A/B Testing
┃
┣ 📂 Retention
┃ ┣ 📂 User Onboarding
┃ ┣ 📂 Email Automation
┃ ┣ 📂 Customer Support
┃ ┣ 📂 Feature Adoption
┃ ┗ 📂 Churn Reduction
┃
┣ 📂 Growth
┃ ┣ 📂 Referral Programs
┃ ┣ 📂 Community Building
┃ ┣ 📂 Product Led Growth
┃ ┣ 📂 Viral Loops
┃ ┗ 📂 Expansion Strategy
┃
┗ 📂 Scaling
┣ 📂 Automation
┣ 📂 Hiring
┣ 📂 Systems
┣ 📂 Global Expansion
┗ 📂 Exit Strategy
Claude Cowork out of the box is good, but with the right context structure, it goes from generic assistant to executive-level partner.
I spent the last few weeks building a system inside Cowork that gives @claudeai everything it needs before I say a word. Who I am. How I write. What I'm working on. My team. My calendar. My priorities. All of it.
Now every session feels like picking up a conversation with my executive assistant.
The difference is context. Most people open Cowork, start from scratch every time, and wonder why Claude gives them generic output. It's not a Claude problem. It's a setup problem.
Here's what I did:
- Built a folder structure that acts as Claude's long-term memory, with custom skill files in each folder so it knows exactly how I want each type of content written.
-Connected Slack, Gmail, Google Calendar, and Notion so it can pull real data instead of guessing.
-Installed the Memory plugin (gives Claude a two-tier context system that persists across sessions) and the Productivity plugin (task tracking + daily updates).
That combination changed everything. Content drafts that used to take 3 rounds now land on the first try.
Meeting prep, email replies, task management. All better because Claude already knows the context.
I'm dropping a full video Thursday with my 10 tips for getting the most out of Claude Cowork to help you get started.
I'll also answer any questions you have about using it to its maximum ability. Comment below.
Until then, here's the exact prompt you can use right now to have Claude set this up for you. Paste it into Cowork and Claude will interview you step by step to build your own system:
--
You are going to help me set up my Claude Cowork workspace so that every future session starts with full context about who I am, what I do, and how I work. We're building a "brain" that makes you useful from the first message.
Here's how this works. You're going to interview me in phases. Ask me questions, then build t…
We’ve seen mainstream adoption of Claude Code across non-eng in the last six weeks at @tryramp. 80% of PMs, 70% of compliance, 55% of the finance team. It’s changed how I think about the role of the data team.
* 2021-2024: analyst says “hey the numbers look off” in #helpdata, someone on the data team digs through code, troubleshoots, and pushes a PR. You could fill your entire day just doing this (and stakeholders were happy and grateful)!
* 2024-2025: analyst says “hey the numbers look off” in #helpdata, someone on the data team copies the question into Claude Code, troubleshoots, and pushes a PR
* Jan 2026: the analysts start by using CC themselves, and show up to #helpdata with “hey, the numbers look off, I know why, and here’s the line of code I think needs to change”
* Feb 2026: “This number looks off, here's why, here's a PR, just approve it"
AI tools are fundamentally changing what’s possible for non-engineering roles. Our finance team ships SQL (shoutout Kate and Jun), our product team conducts causal analysis (Sam, Teddy), our design and engineering team build KPI dashboards. So what does this mean for the data profession, and the size of data teams?
The strongest data ICs are expanding what they are capable of, compressing timelines, shipping front end + back end code, fixing copy, writing SKILL.mds, and driving outcomes. If you are extremely resourceful and curious, have great taste for what matters to our customers, can source your own projects and ship anything in two days (or two hours) I can use 100 of you. You'll be more effective than you've ever been. Something something #jevonsparadox.
If you define yourself by your skillset “I do dbt models and dashboards “I do causal analysis” “I do data eng and Airflow DAGs”, that scope and role is shrinking fast. The existing team can do more, and AI is competing your skillset out.
Paraphrasing @giansegato, we’re progressively automating the easier parts of data jobs to agents, and the complexity and imp…
WELCOME TO THE SKILL ERA OF THE INTERNET
for the last 15 years, if you wanted to build a serious software company, you built a product and exposed an api.
that was the move.
you created functionality… payments, messaging, email, search, analytics… and then you let developers plug into it.
the companies that won owned the pipes.
stripe owned payments.
twilio owned messaging.
sendgrid owned email.
the api was the distribution layer.
once you were integrated, you were embedded.
that model made sense in a world where execution was scarce.
llms compress execution into a prompt.
so the center of gravity shifts.
in this cycle, you build expertise and package it as a skill.
an api is a doorway into a function.
here’s how to send an email.
here’s how to process a payment.
here’s how to fetch this data.
it’s precise. mechanical. bounded.
a skill is a doorway into judgment.
here’s how to audit a landing page like a serious growth operator.
here’s how to structure a legal intake so you catch the real risk.
here’s how to clean and enrich messy directory data so it actually turns into revenue.
you’re encoding a way of thinking.
and that changes how companies are built and how they scale.
in the api era, distribution meant convincing developers to integrate you.
you needed docs. sdk’s. developer evangelism.
you fought for a place inside someone else’s codebase.
in the skill era, distribution means becoming part of someone’s agent workflow.
a founder opens claude code.
they type /seo-audit.
your skill runs.
it frames the output.
it structures the analysis.
it guides the decisions.
your expertise lives inside the execution layer itself.
you aren’t pulling users into your interface.
you’re embedding your thinking into theirs.
that changes company design.
the old playbook looked like this:
build saas
design ui
onboard users
drive retention
expand seats
the new playbook looks more like this:
encode a high-leverage playbook
package it as a skill
let agents c…
Just 28 days without parabans and phthalates turned off breast cancer genes.
Researchers followed a group of healthy women who routinely used common personal-care products containing parabens and phthalates—chemicals found in everything from shampoo and lotion to makeup and fragrance. These compounds can act like estrogen in the body, and excess estrogen-like activity has long been tied to higher breast-cancer risk.
For 28 days, 36 women did one simple thing: they switched to paraben- and phthalate-free alternatives. No drugs, no diet changes—just cleaner cosmetics and toiletries.
The results were striking: urine tests confirmed that levels of the chemicals’ breakdown products plummeted, proving exposure had been sharply reduced.
But the bigger revelation came from breast-tissue biopsies taken before and after the switch. In just four weeks, the women’s breast cells began behaving less like precancerous or cancerous cells.
They regained the ability to respond to normal “cell-death” signals (a safeguard tumors often disable). Protective estrogen receptors, which are typically shut down in breast cancer, switched back on. Gene-expression patterns shifted away from high-risk profiles and toward healthier patterns.
This is the first human evidence that routine exposure to these everyday chemicals can nudge normal breast cells in a cancer-like direction—and, crucially, that removing the exposure can begin to reverse the process remarkably quickly.
It’s not definitive proof that changing your body wash will prevent breast cancer. But it does show that the body notices—and starts to repair itself—almost immediately when you stop putting these substances on your skin.
["Reduction of daily-use parabens and phthalates reverses accumulation of cancer-associated phenotypes within disease-free breast tissue of study subjects." Chemosphere, 2023]
This cartoon mocks the description of the houris (ḥūr al-ʿīn) in Islamic paradise: according to Hadith, every man will have two houris, and their bodies will be so transparent that the bones of their shanks will be visible through the flesh due to their extreme beauty.
This is taken from Sahih al-Bukhari 3245 and Sahih Muslim 2834, which shows that the promise of paradise in Islam is built on sexual greed and patriarchal fantasy, treating women as mere sexual objects and promoting an unrealistic, unhealthy concept of beauty. This proves that Islam is the product of Muhammad’s sexual lust and deception, where paradise is used to lure followers with unlimited sexual rewards for men. Let’s examine this in detail, based on Hadith and Islamic descriptions, which reveal his lust and deception.
1. Description of the Hadith and Its Meaning
Sahih al-Bukhari 3245 states: “Every person will have two wives; the marrow of their shanks will be visible through the flesh and bone due to their extreme beauty.” Sahih Muslim 2834 says: “Every person will have two wives, and the marrow of their shanks will gleam beneath the flesh.” This description emphasizes the sexual allure of paradise women—their beauty is so intense that their bodies become transparent. It is a promise of sexual gratification for men, where women are reduced to sexual objects and symbols of unnatural beauty.
2. The Sexual Description of Islamic Paradise and Greed
The Quran and Hadith describe paradise as a place of sexual pleasure: houris with large eyes, beautiful bodies, and endless sexual relations (Surah Al-Waqi’ah 56:22-24, 35-38; Surah Ar-Rahman 55:56-58). Hadith states that every man will receive 72 houris (Jami’ at-Tirmidhi 2687), with endless sexual relations. This depiction reflects Muhammad’s sexual lust—he promised men unlimited sexual bliss in paradise to attract followers, especially warriors. Descriptions of paradise for women are far fewer and vague, they will only be with their husbands.
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