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Posting for my own amusement. Hopefully, the joy will be shared.
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Daniel Chapell
1.7K posts
Daniel Chapell
@ChapellDaniel
Tech-lifting Wizard • Open Sauce • AI-Augmented Engineer Deep Thoughts & Bad Puns
Remote Katılım Ağustos 2016
96 Takip Edilen145 Takipçiler
@midudev Es verdad, aunque una capacidad de “razonamiento” tan cercana a gpt 5.6 y fable igual es una pasada.
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@ChapellDaniel Me encanta K3, pero mi problema ahora mismo es que la velocidad de salida no es la ideal para el trabajo diario
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Creo que hay 3 modelos de IA que son la clave ahora:
GPT 5.6 Luna (Max), Sol (Medium) y Grok 4.5.
A un nivel de inteligencia muy bueno tienen un coste por tarea bastante bajo y una velocidad de salida por tokens considerable.
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Prompt:
Find 10 emerging Instagram accounts in [category], each with fewer than 1,000 followers.
Monitor every video they publish and track its performance. Identify posts gaining unusual traction relative to the creator’s audience size.
When a post crosses [X views] or begins accelerating rapidly:
1. Extract its core idea, hook, script, structure, pacing, and visual concept.
2. Rewrite the script enough that the result is not an exact copy.
3. Recreate the video in my established style using my authorized face, voice, and media assets.
4. Select whichever channel in our 10M+ follower network is most relevant to the content.
5. Publish the new version.
6. Track the performance of both versions. Add anything that works to our system’s pattern library.
Repeat.
shadcn@shadcn
What happens to creativity when AI makes copying effectively free?
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@shiri_shh Expect more specialized tools targeting narrow tasks inside the standard AI workflow, this wave is about efficiency.
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Most people think AI agents need better LLMs.
They actually need faster search.
When an agent plans a task, it doesn't type one search query like a human. It fires 50–100 queries at once from every angle.
Every search API today wraps thirty-year-old human search stacks behind an endpoint.
They take 240ms to 2.6 seconds per call. Your agent stalls before it even gets to work.
Octen just dropped an API built exclusively for machines
Kuan Zou@KZouAPT
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@thdxr Great leaders make the hard calls and lead by example.
If someone does it with glee though? Yeah… that’s concerning. Might want to get that checked
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@GaryMarcus War has always been defined by the leverage of your technological advantage.
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Ditched Instagram and returned to 𝕏.
The difference in literacy and depth is night and day. It’s been great.
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@matt_gray_ Just like a pond: when the water is still, you see straight to the bottom. When agitated, nothing is clear.
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@KevinSzabo14 The better the writer, the sharper the ability to separate signal from noise and connect the dots.
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Writing makes you realize how stupid you really are and that’s a good thing.
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@blakeaburge Get out of your own head and do something, whatever that is.
Ideas do not work unless you do.
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Major cheat code for life: Realizing no matter how many mistakes you make or how slow you progress, you're still ahead of everyone who isn't trying. Better to look foolish while trying than wonder for the rest of your life what might have happened if you had.
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@shafu0x Tech* turns nerds into millionaires
“Give me a lever long enough and a fulcrum on which to place it, and I shall move the world.”
– Archimedes
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@ODanquah83181 @ChapellDaniel @Ayowinner_ I am curious... what do you think happens when that is one's purpose.. love everyone?
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@ChapellDaniel Every new day is a gift, gratitude changes the way we experience life.
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Waking up to live another day is a privilege — one so often overlooked. Like many simple yet miraculous things in life, it’s easily taken for granted
Be grateful for today
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@PBFcomics Stay with whoever sees you as that telescope looks at hon
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@emonuxui True. With models proliferating at this rate, the technically inclined will have to get really good at separating the needle from the haystack.
Quality over quantity is the new edge.
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@ChapellDaniel Exactly. The challenge is shifting from supervising AI step by step to supervising outcomes and guardrails.
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Software development is trapped in a paradox:
1. We can't process information fast enough to babysit AI in real-time
2. We can’t trust their statistical probability at scale
Modern engineering is figuring out the middle ground.
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@levie YESSS The pattern is clear
hyper-specialized models at low abstraction, scaling up to powerful all-round orchestrators at higher levels.
Specialization at the base, generalization at the top.
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Multi-model agentic systems clearly are the future. Great post by cursor. Their new research shows that a frontier model as a planner and orchestrator with a workhorse cheaper model can materially lower the cost of total tokens on a project, resulting in a 15X cost improvement.
“Few moments in a large task genuinely require frontier intelligence, such as the original decomposition, the design decisions, and certain trade-offs. Once a frontier planner has collapsed the ambiguity into a detailed, explicit instruction, less expensive models simply have to follow it.”
This is increasingly becoming the core design pattern of complex agents, because the tokens used on large amounts of work on many tasks don’t require the same intelligence threshold as the planning step. By routing to different models based on where you are in the task, you get much greater efficiency overall.
This provides the template for where the applied layer in AI will differentiate. You can only drive this efficiency if you know the domain well and have the ability to work with multiple model tiers; companies that can do this will across coding, finance, legal, healthcare, life sciences, and other critical spaces will be in a strong position to get larger workloads that would otherwise be too expensive for the customer to deploy.
Cursor@cursor_ai
We had a team of agents rebuild SQLite from its 835-page manual. It created a replica in Rust which passed 100% of a held-out test suite. Interestingly, cost varied 15x depending on which model mix we used.
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