AI Repo Lens
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AI Repo Lens
@CatToAI2020
Tracking GitHub AI projects, dev tools, and real-world signals. Less hype, more verification.
Se unió Mart 2020
144 Siguiendo166 Seguidores

Building an AI Agent in 2026 is no longer just about picking an LLM.
The real magic happens in the system around the model. 🤖
This roadmap perfectly breaks down how modern AI agents are actually built from scratch. 👇
A production-ready AI agent needs 8 core layers:
1️⃣ Define the Purpose
Before writing prompts, define:
• use case
• user needs
• constraints
• success metrics
Most AI projects fail because this step is skipped.
2️⃣ System Prompt Design
Prompts are becoming operating systems for agents.
A strong system prompt defines:
• role/persona
• goals
• instructions
• safety guardrails
3️⃣ Choose the Right LLM
Different models = different strengths.
• GPT-5.5 → versatility & tool usage
• Claude → reasoning & long context
• Perplexity → research & citations
There’s no “best model.”
Only the best model for the task.
4️⃣ Tools & Integrations
This is where AI becomes actionable.
Agents connected to:
• APIs
• MCP servers
• databases
• custom tools
• external apps
Can actually execute workflows instead of just generating text.
5️⃣ Memory Systems
Memory is the difference between:
“a chatbot”
and
“an intelligent assistant.”
Modern agents use:
• working memory
• vector databases
• structured storage
• episodic memory
6️⃣ Orchestration
This is the hidden layer most people ignore.
Workflows, triggers, queues, retries, routing, multi-agent coordination…
This is what turns prompts into systems.
7️⃣ User Interface
The best AI products win on UX, not just intelligence.
Chat apps, APIs, Slack bots, dashboards - interface matters.
8️⃣ Testing & Evaluations
If you don’t measure quality, latency, reliability & hallucinations…
your AI product will eventually break at scale.
The biggest takeaway?
AI Engineering is rapidly becoming a combination of:
Software Engineering + Prompting + Systems Design + Automation.
The engineers who understand orchestration, memory, tools & workflows will dominate the next decade of AI products.
Save this roadmap.
This is basically the blueprint for building AI agents in 2026. 🚀
Follow @rosemoni18 for more AI engineering breakdowns, prompts, workflows & agent architectures.

English

1998, la 🇫🇷 est championne du monde de football.
Plus de 500 000 supporteurs en communion totale avec les joueurs et le staff.
Pas une poubelle incendiée!
Pas un abris bus détruit!
Pas de tirs de mortiers!
Une liesse populaire, rien d'autre.
Voilà la 🇫🇷 qu'on aime et que l'on veut retrouver.
Français
AI Repo Lens retuiteado

Damn,英伟达和老黄真的是憋了个大的啊,真特么牛逼🤯
今天全网都在转黄仁勋这台拔了电源还能满帧跑 3A 的笔记本,但大多数人看错了重点,游戏其实只是这台机器的糖衣。
真正的核弹是那 128GB 统一内存,它意味着你桌上一台轻薄本,能在本地直接跑起 200B 参数的大模型,这在过去是只有数据中心机柜才干得动的事。
所以 NVIDIA 这次干的根本已经不是又一个游戏本那么简了,它把数据中心那套 Grace CPU 加 Blackwell GPU 整个下放,
1 PetaFLOP 的 FP4 算力、RTX 5070 级的显卡、CPU 和 GPU 共享的统一内存,一起塞进一个能背着走的壳子里。
拔电不掉帧、续航炸裂,这些是讲给所有人听的甜头,这回真正瞄准的,是要在本地跑 AI 的那波人。
如果把游戏本三个字去掉,你会看见一件更大的事。
这就像一个一直只卖发动机的厂商,突然开始造整车,顺手还把高速公路也给铺了,CUDA 是发动机,Grace 是底盘,Windows on Arm 是路,
从今往后你想跑得快,就只能在它修的这条路上跑。
当然,舞台上拔电不掉帧是十分钟的高光,长时间满载会不会降频、ARM 版 Windows 靠兼容层跑老软件会掉多少性能、这套东西最后卖一个什么价,发布会一个都没回答。
但方向已经很清楚了,Intel 和 AMD 还能追性能追制程,但CUDA 攒了十几年的那群开发者肯定是追不上了,
老黄卖的从来不是一台更强的电脑,是一条你用顺手了,就再也下不来的路!
Geeklik ve Ötesine@GeeklikOtesine
NVIDIA, ARM tabanlı yeni işlemcisi RTX Spark'ı duyurdu. - İşlemcide RTX 5070'e denk bir GPU bulunuyor. - Modern oyunlarda 1440P'de 100 FPS'te çalışıyor. - Laptop, Windows olmasına rağmen prizden çektiğinizde performans düşmüyor. - Batarya ömrü uzun. - Sadece laptoplar için değil masaüstü bilgisayarlarını da hedefliyor. - Sahnede 007 First Light ve Forza Horizon 6 ile gösterildi. - Yapay zeka işlem gücü de yüksek. - 2026 Sonbahar'ında çıkacak.
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