Whats AI by Louis-François Bouchard
AI Engineering Foundations: What Every Developer Needs
updated
Normally: tabs everywhere → read releases, scan socials, cite-check, outline, publish
With KIVA: one click expands into related web searches, pulls social discussions, SEO ideas, outlines → done
Tried it on my Gemma 270M news cover last week, workflow took minutes instead of 1.5 hours
👉 Sounds cool? Try KIVA for research: bit.ly/kiva-louis
This video is sponsored, but I recommend trying out KIVA for free even without it.
I’m Louis-François — PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀
#ai #kiva #research
- Most failures come from bad specs, no real data, or models misapplying rules
- Fix it with custom evaluations: JSON checks, tool errors, schema constraints, LLM-as-judge
- Build a dataset, analyze real traces, and turn it into an "Analyze → Measure → Improve" loop
- Best resource I’ve found: Hamel Husain & Shreya Shankar’s course
👉 Grab my 35% discount link here: maven.com/parlance-labs/evals?promoCode=whatsai-louis
This video is sponsored, but this is genuinely the best course for evals out there.
I’m Louis-François — PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀
#ai #llm #evaluations #short
• Nails character consistency while changing outfits, poses, lighting, or scenes
• Benchmarks show it topping image-edit leaderboards
• “Thinking” model → understands intent, even fixes details you didn’t explicitly ask for (“make it look real”)
• Supports multi-turn editing → refine iteratively (e.g. paint walls → then add furniture)
• Creative power: merge up to 3 images, apply styles/textures, blend surreal compositions
• Free in the Gemini app & AI Studio, or super cheap on API at $0.039/image
Sure, small mistakes (text artifacts, cropped details) still happen — like every model. But the image editing focus is a huge leap forward. Definitely not just hype.
I'll use that one for all my thumbnails for now on.
Do you agree? Let me know your current favorite image generation model / app!
I’m Louis-François — PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀
#ai #gemini25 #imageediting #generativeai #google #imagen #texttoimage #aicommunity #short
Master LLMs and Get Industry-ready Now: academy.towardsai.net/?ref=1f9b29
Our ebook: academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29
In this video you’ll learn how Deep Research agents help:
- Save hours of manual research
- Generate structured, citation-backed reports
- Analyze documents, PDFs, and data in context
- Make smarter, faster, better-informed decisions
Learn more for free...
Twitter: https://x.com/Whats_AI
Substack (newsletter): louisbouchard.substack.com
#chatgpt #deepresearch #llm
• Generates controllable 3D environments from text prompts at 720p / 24 FPS
• Worlds are interactive, consistent, and remember past actions (no re-gen where you already explored)
• Runs only for a few minutes with ~1 min of visual memory
• Biggest leap: long-horizon consistency in generated 3D spaces
• But… can’t perfectly simulate real locations or handle multi-agent interactions → limits universality
• More exciting for agents, simulations, and robotics research than for mainstream gaming (at least for now)
Cool progress, overhyped as “gaming’s future”, but definitely a milestone in 3D generation.
I’m Louis-François — PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀
#ai #deepmind #genie3 #short
• Coding support in 80+ languages, 256K context
• Comes with Devstral (agents), Codestral Embed (search), and Mistral Code (LLM) → full coding suite
• Local-friendly + privacy-safe, good for enterprises
• May lag behind giants like Gemini 2.5 out-of-the-box → real value needs customization or retraining, which adds cost
• Best fit for mid/large companies that want to own their coding stack
Promising, but not plug-and-play for everyone. Those are just my thoughts!
I’m Louis-François — PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀
#ai #mistral #codestral #llm #aicode #opensource #aicommunity #short
What’s new:
• Still a similar MoE transformer (671B params, 37B active) with 128K context
• Merges DeepSeek V3 + R1 → one hybrid model with thinking (reasoning) and non-thinking (direct) modes — like what ChatGPT does with GPT-5
• Major upgrades vs V3:
– More efficient reasoning + stronger agent integration
– Post-training boosted tool calling, substantial leaps on coding + tool-use benchmarks
– Long-context quality got big investment: ~630B tokens @32K and ~209B @128K
– Beats R1 in reasoning often times while answering faster
Why it matters:
• Open-source + pricing undercuts GPT-5 and others by 2–6x
• “Thinking” mode enables deep reasoning chains, “non-thinking” mode keeps reliable JSON tool/function calling
• Retrieval & summarization across large docs should degrade less thanks to long-context training
Hype check:
• Probably the best open-source model right now, but benchmarks don’t always translate into real usage (I was underwhelmed whith DeepSeek V3 vs. closed alternatives)
• Tool calling is split:
– Strict JSON calls? Use non-thinking mode
– Special search-agent format? That’s thinking mode only
Verdict:
Definitely not just hype. V3.1 is a strong, cost-efficient open-source contender. But like every model: you’ll need to test it in your own workflows before buying into the leaderboard glow...
Do you use DeepSeek? What do you think? Drop it in the comments 👇
I’m Louis-François, CTO & co-founder at Towards AI. Follow for tomorrow’s no-BS roundup.
#ai #deepseek #deepseekv31
• Family of 3 models:
– Imagen 4 Fast → $0.02 per image, ~2.7s latency (10x faster than Imagen 3)
– Imagen 4 → $0.04 per image
– Imagen 4 Ultra → $0.06 per image, ~10s latency, higher detail (up to 2K res)
• Max 20 API requests / min per project (higher limits possible on request)
• Developers praise the speed, but note persistent text-rendering issues in complex images
Hype check
• Fast model sometimes drifts from prompts — good for quick, simple generations
• Standard & Ultra → better prompt alignment + quality, quite cheap for high-res outputs
• Overall: speed + pricing make this a strong step forward in open access image generation
Weakness is text on screen, especially vs. alternatives like OpenAI's.
Let me know which news I should cover next, and I’ll tag you!
I’m Louis-François, CTO & co-founder at Towards AI. Follow for tomorrow’s no-BS roundup.
#ai #imagen4 #gemini #aistudio #texttoimage #generativeai #google #aicommunity
What’s new:
• Gemini 2.5 can now take links directly in prompts
• Extract the whole page text or search for specific info
• Works via API or Google’s AI Studio, just enable it
• Handles up to 20 links per LLM call
• Works on PDFs and images (but doesn’t actually “see” images or videos inside web pages. Be careful, it may hallucinate based on captions!)
Why it’s interesting:
• Next logical step for context engineering, LLMs pulling structured context in real time
• More flexible than basic internet browsing: you can target specific links for your queries
• With HTML tricks (asking it to give you image URLs, then calling it again), you can build a fairly complete scraper
Limitations / hype check:
• Doesn’t see images or video content on web pages directly
• Won’t crawl multiple subpages automatically (you need to feed links manually)
• Like any scraper, some sites crash it — debugging is hard since you don’t control what it “sees”. Also you sometimes need to say "yes you can do it" for it to understand it can indeed scrape HTML.
• Costs tokens → if you send a long page, you pay more
Verdict: Not a replacement for full web scraping stacks, but a super accessible entry point for developers who just need quick, structured pulls of external data
Do you think this will replace scraping as we know it?
I’m Louis-François, CTO & co-founder at Towards AI. Follow for tomorrow’s no-BS roundup.
#gemini #llm #contextengineering
What’s new
• Big boost in agentic tasks, coding, and reasoning → 74.5% on SWE-bench
• Stronger at multi-file editing & precision (nice for coding!)
• Opus 4.1’s coding focus feels like a practical shift toward real-world use cases (not just raw scale)
Context
• Might signal we’re closer to the pre-training plateau we’ve been talking about
• Some devs even report practical superiority over GPT-5 for coding projects (Jeremy Howard
called it a breath of fresh air vs. gpt-5)
• Nice counterpoint for folks frustrated that ChatGPT forces GPT-5 by default
Hype check
• Solid incremental update, not a revolution
• But if you care about coding & agent workflows → this is a real upgrade
Which one do you prefer, Claude or ChatGPT?
I’m Louis-François, CTO & co-founder at Towards AI. Follow for tomorrow’s no-BS roundup.
#ai #claude #anthropic #claude41 #llm #coding #reasoning #agentic #swebench #gpt5 #aicommunity
Let me explain…
#llm #ai #data
Let’s break it down 👇
What we know:
• Gemma 3 new model: 270M params
• Pretrained + instruction-tuned releases
• Great for text classification & data extraction (specific tasks) while cutting cost/energy
• Strong instruction-following; 128K context like modern LLMs
• Built on the Gemma 3 architecture so easier to fine-tune and use in existing pipelines
• Cost-effective fine-tuning
Caveats / hype check:
• Out-of-the-box on complex tasks is limited → expect more hallucinations
• Ground it with your data (RAG) and fine-tune for best results
• Community notes: “dumb” responses without tuning (expected at this size)
Hype or not?
Not hype! Perfect for simpler tasks you fine-tune it on, and for fast, efficient local inference on smaller devices.
Let me know which news I should cover next, and I’ll tag you!
I’m Louis-François, CTO & co-founder at Towards AI. Follow for tomorrow’s no-BS roundup.
#ai #gemma3 #llm
Let’s break it down 👇
What we know:
• SOTA (?) text→image with a focus on photoreal, “aesthetic photography” vibes
• 12B rectified-flow transformer, directly distilled from [pro] → similar quality explains the strong results
• Collaborative fine-tuning with RLHF
• Claims to match closed solutions like their FLUX1.1 [pro] (not sure)
• Single-GPU friendly; seconds to ~1 min per image
• Variants on HF can speed things up
Not hype / caveats:
• Open weights democratize high-quality photo realism (building on SD & FLUX.1)
• Non-commercial license limits “full openness”
• Quality feels a bit overhyped vs Midjourney or OpenAI’s latest image models
Try it, there are lots of free options online — tell me what you think!
I’m Louis-François, CTO & co-founder at Towards AI. Follow for tomorrow’s no-BS roundup.
#ai #generativeai #texttoimage
Let’s break it down 👇
What we know:
• From a Chinese lab
• MoE design: GLM-4.5 (≈355B total, 32 experts) & GLM-4.5-Air (≈106B, 12 experts) — similar to Kimi K2
• Pushing open-source AI forward
• SOTA open-model results; reportedly rivaling Claude Sonnet 4 on SWE-Bench (?)
• Low API costs (~$0.20–$2.20 /M tokens) with integrations on Hugging Face, ModelScope, OpenRouter — priced like “mini” tiers
• Trained with the Muon optimizer (again like Kimi K2) on high-quality code, synthetic tasks, long-context prompts
• 128K context, hybrid “plan or respond” modes, and fast generation (up to ~100 tok/s)
• Built-in agentic behaviors
My take:
• Feels very close to Kimi K2’s recipe — great for the community, but I’m not swapping my stack yet. Not an ideal size for local models.
• If you’re already running open-weights at this scale, it’s an easy A/B to try
I’m Louis-François, CTO & co-founder at Towards AI. Follow for tomorrow’s no-BS roundup.
#ai #glm45 #llm
We’ll see… but here’s what actually ships 😉
What we know:
• New small reasoning model built from Llama-3.3-70B-Instruct
• Multi-phase post-training: SFT (math/code/science/tool-calling) + RPO/DPO/RLVR for stronger chat, reasoning, and agentic skills
• RLVR + NAS squeeze SOTA-level performance out of a mid-size model (less chasing giant parameter counts)
• Among the top of the 70B class and even outperforms some larger models
• Efficient: single H100 GPU, 128k context
• Fully open + commercial: NVIDIA Open Model License, synthetic (Nemotron-Post-Training-Dataset-v1) + human (HelpSteer3) data, reward models, and RL toolkit
Caveats / hype check:
• 2023 data cutoff → pair with retrieval for freshness
• Leaderboard wins may be overstated if you exclude top closed models (e.g., Claude 4.1, gpt-5)
• Heavy synthetic data: TBD on generalization/bias trade-offs
Why it matters:
• Transparent datasets + tooling → reproducible research
• One-GPU footprint = accessible for labs & smaller companies
• A “small thinking” model that punches above its weight
• Totally open and reproducible
Who should try it?
• If you already host a Llama-70B-ish model, it’s an easy A/B swap to test.
I’m Louis-François, CTO & co-founder at Towards AI. Follow for tomorrow’s no-BS roundup.
#ai #llm #opensource
Qwen-Image is a promising new image generation model. 👇
• 20B-param Multimodal Diffusion Transformer + 7B Qwen-2.5-VL encoder
• Alibaba claims SOTA text rendering + strong editing vs. GPT & Flux
• Trained on billions of curated image–text pairs (Nature 55 %, Design 27 %, People 13 %, Synthetic 5 %)
• New MSRoPE (positional encoding) tech for better resolution scaling & image-text alignment
• Apache-licensed (commercial use OK), FP8 weights lower fine-tune costs
Caveats: heavy GPU needs for top quality, slower inference than smaller open models, “GPT-4o-level” claims likely overblown without broad benchmarks or user testing.
Verdict: solid open-source progress in multimodal generation, but mind the hardware + hype gap.
—
Louis-François — PhD-dropout & CTO/co-founder @ Towards AI
Follow for tomorrow’s no-BS roundup.
#QwenImage #AlibabaAI #GenerativeAI
TL;DR: it's a prompt transforming ChatGPT into a tutor-style chatbot: questions, hints, self-reflection, scaffolded steps instead of a direct answer.
• Personalization + quizzes/feedback to check understanding
• Since it's (for now)_ just a smart prompt → behaviour can vary between users and interactions
• Use it for active learning (think rather than getting answers), not cheating
Verdict: promising for study habits & 24/7 tutor access for improved learning. Excited to see where AI takes us regarding education.
Will AI ruin or improve education? 👇
—
Louis-François — PhD-dropout & CTO/co-founder @ Towards AI
Follow for tomorrow’s no-BS roundup.
#ChatGPT #StudyMode #EdTech
Don’t default to GPT-5. Here’s the quick pick guide 👇
What they all share:
• 400k context
• images + text input, text output only
• tool access
• “Minimal reasoning” toggle = lower cost
Use GPT-5 when…
• You need max reasoning/agent actions & quality over cost
Use GPT-5 Mini for…
• Everyday dev + RAG (the 80/20 sweet spot)
• ~5× cheaper/faster than GPT-5, higher rate limits
• Earlier knowledge cutoff, fine if you rely on web/tools
Use GPT-5 Nano when…
• Bulk summaries, labeling, quick replies at scale
• ~25× cheaper than GPT-5, fastest outputs
TL;DR: Quality = GPT-5. Value = Mini. Speed/scale = Nano.
Save this & thank me later.
—
Louis-François, CTO & co-founder @ Towards AI
Follow for tomorrow’s no-BS roundup.
#GPT5 #OpenAI #LLM
Feels like chatting with a PhD-level expert?
• 400 k context + “think-on-demand” → up to 80 % fewer thinking tokens (cheaper calls!) thanks to improved RL trainings.
• Great with tools as most recent models.
• Best OpenAI coding scores: tops SWE-Bench, 4o, & o3.
• Multimodal (images + video in, text out) with stronger visual reasoning • Higher factual accuracy, calmer tone, fewer 🤯 emojis (finally!!!).
• Knowledge cutoff Sep 30 ’24, but live internet access so not a big deal.
• Free-tier access.
• mini & nano variants too, sharing about which to use tomorrow, follow to know!
Worth switching or overhyped?
—
Louis-François, PhD dropout & CTO/co-founder @ Towards AI.
#GPT5 #OpenAI #GenerativeAI
• 117 B-param MoE (5.1 B active), fits on a single 80 GB GPU
• Near-o4-mini parity on reasoning, coding, math & health tasks
• 128 k context, 3 “effort” levels, full CoT, tool use & code exec
• Trained on English STEM/code, multilingual capacity probably quite bad
• Skip it if energy costs, fine-tuning hurdles, or non-English matter to you; the 20 B may be a smarter first stop for efficiency, but still only trained for English.
—
Louis-François, PhD dropout & CTO @ Towards AI.
Follow for tomorrow’s no-BS roundup.
#OpenAI #gptoss120b #GenerativeAI
• Mixture-of-Experts design → activates only a fraction of params per token; 128k context.
• Runs on ~16 GB VRAM / laptop-class hardware; downloads on Hugging Face.
• Tool use & agent-style workflows (browse/code/execute) are supported.
• Caveats: strongest on text/STEM; multilingual & long-horizon reliability still TBD. Probably worse than other models since training was focused on English.
Verdict: real progress in small-model efficiency + reasoning. Worth trying if you want local/edge deployments (and in english!), just don’t expect closed-model polish on day one.
—
Louis-François — PhD-dropout & CTO/co-founder @ Towards AI.
Follow for tomorrow’s no-BS roundup.
#OpenAI #GPTOSS #LLM #MoE #EdgeAI #AInews #HypeOrNo
• New thinking model that runs parallel hypothesis streams and was re-trained (RL) to coordinate them, not just a prompt trick.
• Results: SOTA on LiveCodeBench v6 and strong on Humanity’s Last Exam (vs models without tool use); also hit IMO (international math Olympiad) gold-medal standard in testing.
• Access: Ultra-tier only in the Gemini app, around $140–$250/month.
My take: useful for deep math/code/science, but for everyday tasks the compute + wait often outweighs gains. Try iterating a few times with standard Gemini 2.5 thinking (or o3) before paying Ultra prices.
Will you try Deep Think? Did it make a big difference for you? 👇
—
Louis-François — PhD-dropout & CTO/co-founder @ Towards AI.
Follow for tomorrow’s no-BS roundup.
#Gemini #DeepThink #Reasoning
My take 👇
• Vibe coding = intuitive, AI-assisted programming
• Great for quick prototyping; doesn’t turn non-coders into pros
• Best results with experienced devs + human verification (unit tests + reviews)
• Not replacing senior engineers, likely replaces some intern-level tasks
• Outcome: either teams ship more with the same headcount, or need fewer people to ship the same product. I bet most companies will choose to ship more work.
What's your take on vibe coding?
—
Louis-François, PhD dropout & CTO/co-founder @ Towards AI.
Follow for tomorrow’s no-BS roundup.
#VibeCoding #ClaudeCode #llms
• Real agentic workflows: plans steps, switches tools, executes
• Why coding is a great agent use-case:
1. the task is complex. beyond chat, you need to code
2. high-value skill (look at software developer’s salaries)
3. LLMs are strong on code, they’ve been trained on lots of it
4. low cost-of-error → there are unit tests and human review before pushing to prod
• Claude code is great for devs to ship more, but only if you learn what it’s doing
• Rate limits & costs: usage can spike—self-reports show $$$; add spend caps, CI tests, and code review. Anthropic recently announced a rate limit for users.
• Fuels the vibe-coding trend—more on that tomorrow! Follow to see it ;)
Are you using Claude Code? What’s your verdict? 👇
—
Louis-François — PhD-dropout & CTO/co-founder @ Towards AI.
Follow for tomorrow’s no-BS roundup.
#ClaudeCode #Anthropic #AIagents
• Prompt→app: generate interactive prototypes & web apps from text (not just mockups).
• Multimodal: attach designs/images in your prompt; it converts them into functional UI & code.
• Great for PoCs and fast iterations, but for larger projects, plan on code review & security checks.
• Real data: built-in Supabase integration lets you add auth, DB, and storage to your prototype.
• Availability: now generally available (with plan-based limits).
Verdict: promising step that democratizes app building. Hype for production, helpful for prototypes.
Did you try Figma Make? Do you agree? 👇
—
Louis-François — PhD-dropout & CTO/co-founder @ Towards AI.
Follow for tomorrow’s no-BS roundup.
#FigmaMake #Figma #NoCode
• In-context video editing/generation with simple prompts (add/remove/transform).
• Big step beyond Gen-3 Alpha: from text-to-video → in-context edits.
• Demos show day→night, rain, reflection removal, green screen, scene extension & new angles.
• Waitlist only so far—expect variability, artifacts on non-curated footage, and high compute costs (which means expensive to use).
• Likely better identity/motion preservation vs older models, but real-world consistency is TBD.
Verdict: Sora-level wow in demos; promising but early. Worth watching, especially for post-production.
Are you joining the waitlist? 👇
—
Louis-François — PhD-dropout & CTO/co-founder @ Towards AI.
Follow for tomorrow’s no-BS roundup.
#Runway #Aleph #GenAI
• Built on Perplexity’s AI search, it implements agentic behaviours like summarizing emails, organizing tabs, shopping/browsing, and auto-browsing with confirmations.
• Early access, invite‑only; $200/mo (Perplexity Max).
• Shift from passive web search to agentic web tasks; comparable to the recent ChatGPT agents. Power comes from Perplexity’s engine (Sonar + external LLMs).
Hype: steep price, limited rollout, demo‑heavy right now; same privacy/safety concerns as any tool that can act for you.
Value: if it genuinely saves you more than $200/month in time, power users may love it. Cool to see competition with OpenAI and other agent-focused big companies.
Will you try it? 👇
—
Louis‑François — PhD‑dropout & CTO/co‑founder @ Towards AI.
Follow for tomorrow’s no‑BS roundup.
#Perplexity #Comet #AIagents
• Segments with natural‑language prompts (vs Meta SAM’s points/clicks/boxes/masks).
• Handles relationships, conditionals, OCR text, and multilingual labels.
• Great for prototyping and LLM apps; edge/real‑time still favors YOLO/SAM.
• Expect some run‑to‑run variability; not ideal for always‑on camera pipelines.
• Best use: gemini‑2.5‑flash, avoid “thinking” mode, request structured JSON.
Prompt to paste:
Give the segmentation masks for the objects.
Output a JSON list of segmentation masks where each entry contains the 2D bounding box in the key "box_2d", the segmentation mask in key "mask", and the text label in the key "label".
Use descriptive labels.
Hype or not? Tell me where you’d use this 👇
—
I'm Louis‑François, PhD dropout turned CTO & co‑founder @ Towards AI. Follow for tomorrow’s no‑BS roundup.
#Gemini #GoogleAI #ComputerVision
It is the first open‑source trillion‑parameter MoE model
• 1 T total / 32 B active parameters → giant yet efficient
• Outscores GPT‑4.1 & Claude‑4 (65.8 % SWE‑Bench, 53.7 % LiveCodeBench)
• 128 k context, MIT‑licensed, no vision implemented… yet
• Open weights, but self‑hosting needs mega GPUs, so most will hit the API
Hype or next DeepSeek moment? 👇
Good morning, I’m Louis-François, ex-PhD researcher turned AI builder at Towards AI. Follow for tomorrow’s no-BS roundup.
#ai #llm #MoE
►Full article and references: louisbouchard.ai/the-year-of-ai-agents
► Our new book Building LLMs for Production: amzn.to/4bqYU9b
►Twitter: twitter.com/Whats_AI
►My Newsletter (My AI updates and news clearly explained): louisbouchard.substack.com
►Join Our AI Discord: discord.gg/learnaitogether
How to start in AI/ML - A Complete Guide:
►louisbouchard.ai/learnai
Become a member of the YouTube community, support my work and get a cool Discord role :
youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join
#ai #agent #llms
Master LLMs and Get Industry-ready Now: academy.towardsai.net/?ref=1f9b29
Our ebook: academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29
Learn more for free...
Twitter: https://x.com/Whats_AI
Substack (newsletter): louisbouchard.substack.com
#ai #generativeai #llm
Master LLMs and Get Industry-ready Now: academy.towardsai.net/?ref=1f9b29
Our ebook: academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29
Learn more for free...
Twitter: https://x.com/Whats_AI
Substack (newsletter): louisbouchard.substack.com
#ai #generativeai #llm
Master LLMs and Get Industry-ready Now: academy.towardsai.net/?ref=1f9b29
Our ebook: academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29
Learn more for free...
Twitter: https://x.com/Whats_AI
Substack (newsletter): louisbouchard.substack.com
#ai #generativeai #llm
Personalization, databases, retrieval (RAG, CAG), frameworks, fine-tuning…
We will discuss:
LLM limitations
Context window
Knowledge issues
Embeddings + encoders
Long context
RAG
Data, vector databases, indexing, retrieval
CAG (cached context)
Fine-tuning
RLFT
In five intensive sessions, learn how to go from a simple prompt all the way to production. Master the fundamentals, Retrieval-Augmented Generation (RAG), evaluation, agent workflows, and large-language-model optimization.
Although there’s a “for developers” angle, the course is relevant to everyone—no prior experience required. A bit of Python knowledge helps but isn’t essential.
Learn more for free...
Twitter: https://x.com/Whats_AI
Substack (newsletter): louisbouchard.substack.com
#generativeai #rag #LLM
►Colab Notebook: colab.research.google.com/drive/1zBxl-7mU5rgl7V_zVXRfasHmFBFz7Tsv?usp=sharing
►Full article and references: theneuralmaze.substack.com/p/rag-vs-cag-a-deep-technical-breakdown
► Our new book Building LLMs for Production: amzn.to/4bqYU9b
►Twitter: twitter.com/Whats_AI
►My Newsletter (My AI updates and news clearly explained): louisbouchard.substack.com
►Join Our AI Discord: discord.gg/learnaitogether
How to start in AI/ML - A Complete Guide:
►louisbouchard.ai/learnai
Become a member of the YouTube community, support my work and get a cool Discord role :
youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join
#llm #rag #cag
Full article and references: louisbouchard.ai/mcp
More links:
• MCP 101: descope.com/learn/post/mcp
• MCP explained in 15 mins (video): youtube.com/watch?v=GQDHxlKJe_M
• Deepdive into MCP authorization: descope.com/blog/post/mcp-auth-spec
• A2A 101: descope.com/learn/post/a2a
• Enterprise Challenges in Deploying Remote MCP Servers: descope.com/blog/post/enterprise-mcp
► Our new book Building LLMs for Production: amzn.to/4bqYU9b
►Twitter: twitter.com/Whats_AI
►My Newsletter (My AI updates and news clearly explained): louisbouchard.substack.com
►Join Our AI Discord: discord.gg/learnaitogether
How to start in AI/ML - A Complete Guide:
►louisbouchard.ai/learnai
Become a member of the YouTube community, support my work and get a cool Discord role :
youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join
#agent #llm #mcp
In five intensive sessions, learn how to go from a simple prompt all the way to production. Master the fundamentals, Retrieval-Augmented Generation (RAG), evaluation, agent workflows, and large-language-model optimization.
Although there’s a “for developers” focus, the course is still relevant to everyone—no prior knowledge required. A basic grasp of Python helps but isn’t essential to get value from the training.
Learn more for free...
Twitter: https://x.com/Whats_AI
Substack (newsletter): louisbouchard.substack.com
#generativeAI #AI #LLM
► Read the article version: louisbouchard.ai/reasoning-models
► Join our upcoming Agents course (email or DM me to get in!): academy.towardsai.net/courses/agent-engineering?ref=1f9b29
►Twitter: twitter.com/Whats_AI
►My Newsletter (My AI updates and news clearly explained): louisbouchard.substack.com
►Join Our AI Discord: discord.gg/learnaitogether
How to start in AI/ML - A Complete Guide:
►louisbouchard.ai/learnai
Become a member of the YouTube community, support my work and get a cool Discord role :
youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join
#llm #llms #reasoningmodels
Master LLMs and Get Industry-ready Now: academy.towardsai.net/?ref=1f9b29
Our ebook: academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29
Learn more for free...
Twitter: https://x.com/Whats_AI
Substack (newsletter): louisbouchard.substack.com
#ai #generativeai #llm
Master LLMs and Get Industry-ready Now: academy.towardsai.net/?ref=1f9b29
Our ebook: academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29
Learn more for free...
Twitter: https://x.com/Whats_AI
Substack (newsletter): louisbouchard.substack.com
#ai #generativeai #llm
Master LLMs and Get Industry-ready Now: academy.towardsai.net/?ref=1f9b29
Our ebook: academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29
Learn more for free...
Twitter: https://x.com/Whats_AI
Substack (newsletter): louisbouchard.substack.com
#ai #generativeai #llm
Master LLMs and Get Industry-ready Now: academy.towardsai.net/?ref=1f9b29
Our ebook: academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29
Learn more for free...
Twitter: https://x.com/Whats_AI
Substack (newsletter): louisbouchard.substack.com
#ai #python #llm
Master LLMs and Get Industry-ready Now: academy.towardsai.net/?ref=1f9b29
Our ebook: academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29
Learn more for free...
Twitter: https://x.com/Whats_AI
Substack (newsletter): louisbouchard.substack.com
#ai #python #llm
► Master the most in-demand skill for building AI-powered solutions—from scratch: academy.towardsai.net/courses/python-for-genai?ref=1f9b29
► Master LLMs and Get Industry-ready Now: academy.towardsai.net/?ref=1f9b29
►Twitter: twitter.com/Whats_AI
►My Newsletter (My AI updates and news clearly explained): louisbouchard.substack.com
►Join Our AI Discord: discord.gg/learnaitogether
Become a member of the YouTube community, support my work and get a cool Discord role :
youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join
#quantum #gtc #gtc25


