Saman Ahmed

23 days ago

Last year, I built my LinkedIn audience to 50K followers. I spent years writing content, networking, learning, and showing up every single day. Then one day… My account got banned. Everything disappeared overnight. Honestly, it hurt a lot. Because people only see the followers. They don’t see the hard work behind it. I stopped for a while. I questioned myself. I lost motivation. But deep down, I knew one thing: I didn’t want to quit. So I decided to start again from zero. New profile. New beginning. Same dream. I know rebuilding won’t be easy. But this time I’m doing it with more experience, more clarity, and a stronger mindset. I just hope I can get the same love and support again on this journey ❤️

117

ScaleWthAI retweeted

about 9 hours ago

https://t.co/0AYLklAjau

about 9 hours ago

@charliejhills Level 1 is so underrated fr

about 23 hours ago

@charliejhills woooow that's a lot of free learning

ScaleWthAI retweeted

about 23 hours ago

Anthropic just dropped Code w/ Claude Day 2. All 13 sessions from London 2026 in one place: 1. Ship your first Managed Agent ↳ https://t.co/RvANkXsUnO 2. Tool, skill, or subagent? ↳ https://t.co/Tf0MYUWe69 3. Agent Battle: Mine the most diamonds ↳ https://t.co/lCY1r13mCf 4. Evals for taste: slide-generation agent ↳https://t.co/lCY1r13mCf 5. Agents that remember ↳ https://t.co/RaK7gAHZ6q 6. How we Claude Code ↳ https://t.co/1mB2GL10po 7. Agentic workflows with a custom DSL ↳ https://t.co/6L7OMnqtJa 8. Fighting financial crime with Claude Cowork ↳ https://t.co/y4bHXjQ0Xt 9. AI at the legal-technical frontier ↳ https://t.co/vqWAQmuS7b 10. How AirOps builds AI products with Claude ↳ https://t.co/p3USU2avAI 11. How Metaview built self-improving prompts ↳ https://t.co/nOgmLcHdWu 12. Teaching agents to learn from your team ↳https://t.co/KRukgqhto3 13. Building the best agentic analytics harness ↳ https://t.co/fvB5QKfg0E Full playlist: https://t.co/BH9OML9z8K Get 80+ free resources https://t.co/zr1uWatiBo Repost ♻️ to help someone in your network. P.S. Which session are you watching first?

12K

ScaleWthAI retweeted

1 day ago

Claude now has 4 models and 5 Effort levels. Pick wrong and you burn your limits 2x faster: ✦ Haiku 4.5 Use it for quick, simple tasks. ☑︎ It answers in seconds and costs the least. ☒ It lacks the depth for complex work. ✦ Sonnet 4.6 Use it for everyday work. ☑︎ It is the default and drains limits the slowest. ☒ It caps out on the hardest reasoning. ✦ Opus 4.8 Use it for complex reasoning. ☑︎ It thinks longer and delivers on hard work. ☒ It is slower and spends your limits faster. ✦ Fable 5 Use it for your toughest, longest jobs. ☑︎ It plans, builds, and tests its own work. ☒ It is overkill unless you live in Claude Code. The lineup feels like OpenAI a year ago, when nobody knew which model to use. So here's my playbook for testing: 1. Run Sonnet for your day-to-day work. 2. Switch to Opus for the harder problems. 3. Save Fable for Claude Code on the 20x Max plan. Pick the model first, then set how hard it should think. Remember 2 numbers before you switch. - Fable burns your usage 2x faster than Opus. - Access ends June 22, so do not get reliant on it. 100+ free Claude guides https://t.co/YhLRwAtaHw Repost ♻️ to help someone in your network. P.S. Which model are you defaulting to right now?

10K

1 day ago

@charliejhills This is the clearest breakdown i've seen so far

2 days ago

@socialwithaayan Ai agents are getting powerful enough that skill security can't be optional anymore.

2 days ago

@charliejhills This could make scenario planning a lot more practical

ScaleWthAI retweeted

2 days ago

A CHINESE QUANT STUDENT LITERALLY BUILT A SOCIETY INSIDE A COMPUTER. There's a repo on GitHub blowing up right now. Its name is MiroFish. A Chinese college student built it in 10 days. It's open source. And it just raised $4,000,000. It runs thousands of AI agents simultaneously, each with their own memory and behavior, simulating how real crowds think, debate, and react. Here's how it works. You feed it any scenario. A news leak. A policy change. A PR crisis. A novel's missing ending. It doesn't summarize. It doesn't guess. It runs a full simulation. Agents debate each other. Shift positions. React in real time. Out comes a forecast of how a crowd actually moves through that event. You don't touch a thing. Now, why is this different from every other AI prediction tool. Because normally, the process works like this. One model reads the news. One model outputs a sentiment score. One model generates a probability. Each one working alone, with no memory, no behavior, no interaction. The result is a guess dressed up as analysis. MiroFish runs the whole room at once. Thousands of agents, each behaving like an individual, producing crowd-level outcomes no single model can replicate. Trading desks are using this to simulate market reactions before placing positions. PR teams are running crisis scenarios before statements go out. Researchers are modeling public response to policy before it passes. The difference is: They're building this infrastructure from scratch. You're not. The builder is a college student with a quant background. 10 days of work. $4M raised. A GitHub repo the developer community is spreading without being asked. Setup is one clone away. Search MiroFish on GitHub. Install, run, simulate it's completely in your hands. → https://t.co/MvU53ZgxYL

charliejhills's tweet photo. A CHINESE QUANT STUDENT LITERALLY BUILT A SOCIETY INSIDE A COMPUTER.

There's a repo on GitHub blowing up right now. Its name is MiroFish.

A Chinese college student built it in 10 days.

It's open source. And it just raised $4,000,000.

It runs thousands of AI agents simultaneously, each with their own memory and behavior, simulating how real crowds think, debate, and react.

Here's how it works.

You feed it any scenario. A news leak. A policy change. A PR crisis. A novel's missing ending.

It doesn't summarize. It doesn't guess.

It runs a full simulation. Agents debate each other. Shift positions. React in real time. Out comes a forecast of how a crowd actually moves through that event.

You don't touch a thing.

Now, why is this different from every other AI prediction tool.

Because normally, the process works like this.

One model reads the news. One model outputs a sentiment score. One model generates a probability. Each one working alone, with no memory, no behavior, no interaction.

The result is a guess dressed up as analysis.

MiroFish runs the whole room at once. Thousands of agents, each behaving like an individual, producing crowd-level outcomes no single model can replicate.

Trading desks are using this to simulate market reactions before placing positions. PR teams are running crisis scenarios before statements go out. Researchers are modeling public response to policy before it passes.

The difference is:

They're building this infrastructure from scratch. You're not.

The builder is a college student with a quant background. 10 days of work. $4M raised. A GitHub repo the developer community is spreading without being asked.

Setup is one clone away.

Search MiroFish on GitHub. Install, run, simulate it's completely in your hands.

→ https://t.co/MvU53ZgxYL

3 days ago

@charliejhills yesss, the FireRed detail got me

ScaleWthAI retweeted

3 days ago

Crazy! Claude Mythos is finally here, just wearing a different name. Fable 5 is the public version of the model Anthropic called too dangerous to ship. It's the first Claude to break 90% on their hardest analytics test. It even beat Pokémon FireRed start to finish on vision alone, a game older models couldn't finish even with a harness. Hand that to @hyperagentapp, set a goal, let it loop for hours. #HyperagentPartner

15K

3 days ago

@socialwithaayan @AnalemmaAI Lemma looks OP for researchers 🔥

3 days ago

@socialwithaayan Mate, no debugging no back and forth, That sentence alone sold me on trying it :))

ScaleWthAI retweeted

4 days ago

Bigger models aren't smarter just because they're bigger. They're smarter because they forget less. A new paper from Stanford, MIT, Harvard, and Anthropic gives the clearest training-based answer yet to a question that's bugged people for a while: why do large models pick up skills that small models miss? The answer comes down to what happens to rare tasks during training. They get learned, then immediately overwritten. Here's the mechanism. Every time a common pattern shows up in the data, it updates the same neurons a rare task was just starting to use. Small models have limited internal space, and common tasks claim it first. So a rare signal appears, gets partially learned, and gets erased before it shows up again. The model never gets enough repetitions to lock it in. Bigger models have more room. Once they've allocated enough capacity to the common tasks, the updates for those tasks get weaker and stop trampling everything else. Rare signals survive longer, accumulate, and eventually consolidate into something the model can actually build on. The researchers tested this two ways. First on controlled toy tasks where they could dial rarity and complexity up and down by hand. Then on OLMo language models from 4M to 4B parameters. Same result both times: bigger models showed less gradient interference and held onto more task-specific features in their representations. What I find most interesting is the part about memorization. The paper doesn't frame it as the enemy. For rare tasks, holding onto partial memories of sparse examples is what lets the model eventually form a general rule. Memory first, abstraction later. So the question was never whether a small model could hold a rare skill in principle. It's whether training ever lets it keep the skill long enough to do anything with it. https://t.co/3haXkYgIrm

charliejhills's tweet photo. Bigger models aren't smarter just because they're bigger.

They're smarter because they forget less.

A new paper from Stanford, MIT, Harvard, and Anthropic gives the clearest training-based answer yet to a question that's bugged people for a while: why do large models pick up skills that small models miss?

The answer comes down to what happens to rare tasks during training. They get learned, then immediately overwritten.

Here's the mechanism. Every time a common pattern shows up in the data, it updates the same neurons a rare task was just starting to use. Small models have limited internal space, and common tasks claim it first. So a rare signal appears, gets partially learned, and gets erased before it shows up again. The model never gets enough repetitions to lock it in.

Bigger models have more room. Once they've allocated enough capacity to the common tasks, the updates for those tasks get weaker and stop trampling everything else. Rare signals survive longer, accumulate, and eventually consolidate into something the model can actually build on.

The researchers tested this two ways. First on controlled toy tasks where they could dial rarity and complexity up and down by hand. Then on OLMo language models from 4M to 4B parameters. Same result both times: bigger models showed less gradient interference and held onto more task-specific features in their representations.

What I find most interesting is the part about memorization. The paper doesn't frame it as the enemy. For rare tasks, holding onto partial memories of sparse examples is what lets the model eventually form a general rule. Memory first, abstraction later.

So the question was never whether a small model could hold a rare skill in principle. It's whether training ever lets it keep the skill long enough to do anything with it.

https://t.co/3haXkYgIrm

11K

4 days ago

@charliejhills Memory first, abstraction later loooove this

ScaleWthAI retweeted

4 days ago

I wasted 6 months on Claude Code slop. A free plugin fixed it in 30 seconds: It's called Superpowers (built by Jesse Vincent). It reached 208k stars on GitHub in 8 months. Forces the agent to plan before any code. Here's exactly how the 7 stages work: Step 1. Brainstorm ✦ Asks you questions to refine the spec ✦ Locks nothing in until the design is clear Step 2. Worktrees ✦ Spins up an isolated branch for the work ✦ Verifies the test baseline is clean Step 3. Write the plan ✦ Breaks work into 2 to 5 minute tasks ✦ Lists exact file paths and steps per task Step 4. Subagent ✦ Sends each task to a fresh helper agent ✦ Reviews the spec first, then code quality Step 5. Run TDD ✦ Red, green, refactor: test, code, then cleanup ✦ Deletes any code shipped before a test Step 6. Code review ✦ Checks every change against the plan ✦ Blocks progress on critical issues Step 7. Ship it ✦ Runs the test suite and confirms it passes ✦ Opens a PR or merges, then cleans the branch The result: Claude Code stops shipping slop. Install it in one line: /plugin install superpowers@claude-plugins-official Free repo → https://t.co/tGNwFVYsEa Repost ♻️ to help someone in your network.

charliejhills's tweet photo. I wasted 6 months on Claude Code slop.

A free plugin fixed it in 30 seconds:

It's called Superpowers (built by Jesse Vincent).
It reached 208k stars on GitHub in 8 months.

Forces the agent to plan before any code.

Here's exactly how the 7 stages work:

Step 1. Brainstorm
✦ Asks you questions to refine the spec
✦ Locks nothing in until the design is clear

Step 2. Worktrees
✦ Spins up an isolated branch for the work
✦ Verifies the test baseline is clean

Step 3. Write the plan
✦ Breaks work into 2 to 5 minute tasks
✦ Lists exact file paths and steps per task

Step 4. Subagent
✦ Sends each task to a fresh helper agent
✦ Reviews the spec first, then code quality

Step 5. Run TDD
✦ Red, green, refactor: test, code, then cleanup
✦ Deletes any code shipped before a test

Step 6. Code review
✦ Checks every change against the plan
✦ Blocks progress on critical issues

Step 7. Ship it
✦ Runs the test suite and confirms it passes
✦ Opens a PR or merges, then cleans the branch

The result: Claude Code stops shipping slop.

Install it in one line:

/plugin install superpowers@claude-plugins-official

Free repo → https://t.co/tGNwFVYsEa

Repost ♻️ to help someone in your network.

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202

16K

4 days ago

@charliejhills loooove the worktree step

5 days ago

@charliejhills Feels like we're slowly moving from choosing models to choosing outcomes the system decides the rest.

6 days ago

@charliejhills Bookmarked!!

ScaleWthAI retweeted