Anthropic pays $750,000+ a year for engineers who can build LLM architectures from scratch.
This 2-hour Stanford lecture gives you the exact pipeline LLM engineers get paid $750K/year for.
Data + architecture + scaling laws + post-training.
Bookmark it & watch today. Then read article below.
We will continue to see layoffs from companies with large eng teams
Engineers are producing 10-100x the code they used to....
This is causing a lot of instability in companies with large code bases and teams
The status-quo is simply not sustainable
Salynt is preparing to run its first neuroimaging pilot with 1–2 major hospitals in the DMV area.
Now, we need a strategic angel investor willing to help support one of these pilots and help get us across the line.
Goal: Pilot → first enterprise client → close seed round.
We are cooked… this is 100% AI
PROMPT: "A professor writes out a mathematical proof for trigonometric identities on a traditional chalkboard, explaining the step he is currently on in the equation."
I have been fine-tuning LLMs for over 2 years now!
Here are the top 5 LLM fine-tuning techniques, explained with visuals:
First of all, what's so different about LLM finetuning?
Traditional fine‑tuning is impractical for LLMs (billions of params; 100s GB).
Since this kind of compute isn't accessible to everyone, parameter-efficient finetuning (PEFT) came into existence.
Before we go into details of each technique, here's some background that will help you better understand these techniques:
LLM weights are matrices of numbers adjusted during finetuning.
Most PEFT techniques involve finding a lower-rank adaptation of these matrices, a smaller-dimensional matrix that can still represent the information stored in the original.
Now with a basic understanding of the rank of a matrix, we're in a good position to understand the different finetuning techniques.
(refer to the image below for a visual explanation of each technique)
1) LoRA
- Add two low-rank trainable matrices, A and B, alongside weight matrices.
- Instead of fine-tuning W, adjust the updates in these low-rank matrices.
Even for the largest of LLMs, LoRA matrices take up a few MBs of memory.
2) LoRA-FA
While LoRA significantly decreases the total trainable parameters, it requires substantial activation memory to update the low-rank weights.
LoRA-FA (FA stands for Frozen-A) freezes matrix A and only updates matrix B.
3) VeRA
- In LoRA, low-rank matrices A and B are unique for each layer.
- In VeRA, A and B are frozen, random, and shared across all layers.
- Instead, it learns layer-specific scaling VECTORS (b and d) instead.
4) Delta-LoRA
- It tunes the matrix W as well, but not in the traditional way.
- Here, the difference (or delta) between the product of matrices A and B in two consecutive training steps is added to W.
5) LoRA+
- In LoRA, both matrices A and B are updated with the same learning rate.
- Authors of LoRA+ found that setting a higher learning rate for matrix B results in better convergence.
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Find me → @_avichawla
Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.
We are not slowing down in December.
- OpenAI to release a GPT-5.2 to challenge Gemini 3
- SOTA video model from Kling
- Amazon's new Nova model just dropped!
- DeepSeek will launch an agentic version of V3.2
The Party Never Stops....
gemini 3 just made every $15k ai consultant look like a clown
google silent-dropped autonomous agents to 650 million users yesterday
what consultants charge $15K and 6 weeks to "implement" now takes 4 minutes on a phone
here's what actually changed:
the model:
→ plans multi-step workflows autonomously
→ executes start to finish with zero hand-holding
→ optimized for non-experts (no CS degree needed)
→ already live on mobile canvas feature
while "AI agencies" are charging $8k-20k for strategy decks, google just deployed real automation to more people than chatgpt's entire user base
the intelligence gap is getting stupid:
that consultant billing $200/hr to "set up AI workflows" → the app does it autonomously now
that agency charging $15k for "custom AI implementation" → built in 4 minutes on gemini 3 mobile
that bootcamp selling "learn AI automation" for $2k → obsolete before the course launched
some startup just replaced their $18k/month AI consulting retainer with a free app
same output. 4 minute setup. zero technical knowledge required.
most businesses still think AI automation needs:
- 6 month roadmaps
- technical teams
- consulting firms
- $50k+ budgets
reality: it needs a phone and 4 minutes
your competition doesn't know this exists yet
but they will
comment "GEMINI" and i'll send you the breakdown of how to use this before everyone figures it out
Incredible experience touring Sibley Memorial Hospital with JHWI. From oncology to maternal health to innovation labs. My team and I left inspired, aligned, and even more committed to pushing what AI & virtual reality can do for healthcare. #Salynt#ai#startups#fundraising
What is an AI Agent? 🤖📘
AI Agents are the future of automation — they think, act & learn like humans ⚡
Also I’ve compiled 1000+ Materials — including AI Agents, LLMs, Prompting, SQL & Automation Guides 🚀
To get it 👇
1️⃣ Follow me (@daievolutionhub) so I can DM you
2️⃣ Repost this post 🔁
3️⃣ Comment “AI” 💬
#AI #AIAgent #LLM #MachineLearning #Automation #Tech
Everyone's debating whether AI will replace jobs...
Meanwhile, AI is busy making every developer 10x more productive and turning junior engineers into senior-level contributors overnight!!
The real revolution isn't replacement - it's radical empowerment 🚀
AI Agents, the practical guidebook
5 patterns. 5 levels. 12 real projects.
Everything shown step by step👇
What's inside?
1. What is an AI Agent?
2. Agent vs LLM vs RAG
3. LLM (Large Language Model)
4. RAG (Retrieval-Augmented Generation)
5. Building blocks of AI Agents
↳ Role-playing
↳ Focus/Tasks
↳ Tools (Custom tools, via MCP)
↳ Cooperation
↳ Guardrails
↳ Memory
6. Agentic AI Design Patterns
↳ Reflection pattern
↳ Tool use pattern
↳ ReAct (Reason and Act) pattern
↳ Planning pattern
↳ Multi-Agent pattern
7. Levels of Agentic AI Systems
↳ Basic responder
↳ Router pattern
↳ Tool calling
↳ Multi-agent pattern
↳ Autonomous pattern
8. AI Agents Projects
↳ Agentic RAG
↳ Voice RAG Agent
↳ Multi-agent Flight finder
↳ Financial Analyst
↳ Brand Monitoring System
↳ Multi-agent Hotel Finder
↳ Multi-agent Deep Researcher
↳ Human-like Memory for Agents
↳ Multi-agent Book Writer
↳ Multi-agent Content Creation System
↳ Documentation Writer Flow
↳ News Generator
Comment "Agents" and I'll DM you the 117 pages PDF.
Guidebook created by Daily Dose of Data Science.
♻ Repost if you found this useful.
Grok is evolving much faster than any other AI.
If this rate of progress continues, @xAI will outpace other AI companies by a significant margin.
App upgrades are roughly on par with internal upgrades.
DeepAgent one-shots this ENTIRE presentation with all the graphs and photos.
This is STRICTLY BETTER than what a human can do in 5 hours.
In a couple of weeks, this will get even better!!