Kris

Founder and CEO of Sikt Intelligence

[Kris @AllAbtAI]

learn more at https://t.co/8pCr7BnvaE

[Kris @AllAbtAI]

more likely than republicans beating democrats in midterms?

#midterms #siktintelligence

[Kris @AllAbtAI]

@VibeDevolli @Polymarket @HyperliquidX @grok cursor?:)

[Kris @AllAbtAI]

@TheAIMerchants well done! i will remove this message so other can try too, ggg

[Kris @AllAbtAI]

@SpaceX 🔥🔥🔥

[Cursor @cursor_ai]

OpenAI’s new o1 models are available in Cursor!

We’ve found o1 to be excellent at well-specified, reasoning-intense problems. We still recommend sonnet/4o for most tasks.

We’re initially rolling out the models with usage-based pricing but will iterate as rate limits increase.

[AI at Meta @AIatMeta]

Starting today, open source is leading the way. Introducing Llama 3.1: Our most capable models yet.

Today we’re releasing a collection of new Llama 3.1 models including our long awaited 405B. These models deliver improved reasoning capabilities, a larger 128K token context window and improved support for 8 languages among other improvements. Llama 3.1 405B rivals leading closed source models on state-of-the-art capabilities across a range of tasks in general knowledge, steerability, math, tool use and multilingual translation.

The models are available to download now directly from Meta or @huggingface. With today’s release the ecosystem is also ready to go with 25+ partners rolling out our latest models — including @awscloud, @nvidia, @databricks, @groqinc, @dell, @azure and @googlecloud ready on day one.

More details in the full announcement ➡️ https://t.co/hhJoLm5eLV
Download Llama 3.1 models ➡️ https://t.co/rRjvmxqCTC

With these releases we’re setting the stage for unprecedented new opportunities and we can’t wait to see the innovation our newest models will unlock across all levels of the AI community.

[Kris @AllAbtAI]

🔥🔥

[Andrej Karpathy @karpathy]

📽️ New 4 hour (lol) video lecture on YouTube:
"Let’s reproduce GPT-2 (124M)"
https://t.co/QTUdu8b0qh

The video ended up so long because it is... comprehensive: we start with empty file and end up with a GPT-2 (124M) model:
- first we build the GPT-2 network
- then we optimize it to train very fast
- then we set up the training run optimization and hyperparameters by referencing GPT-2 and GPT-3 papers
- then we bring up model evaluation, and
- then cross our fingers and go to sleep.
In the morning we look through the results and enjoy amusing model generations. Our "overnight" run even gets very close to the GPT-3 (124M) model. This video builds on the Zero To Hero series and at times references previous videos. You could also see this video as building my nanoGPT repo, which by the end is about 90% similar.

Github. The associated GitHub repo contains the full commit history so you can step through all of the code changes in the video, step by step.
https://t.co/BOzkxQ8at2

Chapters.
On a high level Section 1 is building up the network, a lot of this might be review. Section 2 is making the training fast. Section 3 is setting up the run. Section 4 is the results. In more detail:
00:00:00 intro: Let’s reproduce GPT-2 (124M)
00:03:39 exploring the GPT-2 (124M) OpenAI checkpoint
00:13:47 SECTION 1: implementing the GPT-2 nn.Module
00:28:08 loading the huggingface/GPT-2 parameters
00:31:00 implementing the forward pass to get logits
00:33:31 sampling init, prefix tokens, tokenization
00:37:02 sampling loop
00:41:47 sample, auto-detect the device
00:45:50 let’s train: data batches (B,T) → logits (B,T,C)
00:52:53 cross entropy loss
00:56:42 optimization loop: overfit a single batch
01:02:00 data loader lite
01:06:14 parameter sharing wte and lm_head
01:13:47 model initialization: std 0.02, residual init
01:22:18 SECTION 2: Let’s make it fast. GPUs, mixed precision, 1000ms
01:28:14 Tensor Cores, timing the code, TF32 precision, 333ms
01:39:38 float16, gradient scalers, bfloat16, 300ms
01:48:15 torch.compile, Python overhead, kernel fusion, 130ms
02:00:18 flash attention, 96ms
02:06:54 nice/ugly numbers. vocab size 50257 → 50304, 93ms
02:14:55 SECTION 3: hyperpamaters, AdamW, gradient clipping
02:21:06 learning rate scheduler: warmup + cosine decay
02:26:21 batch size schedule, weight decay, FusedAdamW, 90ms
02:34:09 gradient accumulation
02:46:52 distributed data parallel (DDP)
03:10:21 datasets used in GPT-2, GPT-3, FineWeb (EDU)
03:23:10 validation data split, validation loss, sampling revive
03:28:23 evaluation: HellaSwag, starting the run
03:43:05 SECTION 4: results in the morning! GPT-2, GPT-3 repro
03:56:21 shoutout to llm.c, equivalent but faster code in raw C/CUDA
03:59:39 summary, phew, build-nanogpt github repo

[Kris @AllAbtAI]

@MatthewBerman 🔥🔥🔥

[Andrew Ng @AndrewYNg]

New agentic short course! Multi AI Agent Systems with crewAI, built with @crewAIInc's founder and CEO @joaomdmoura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute.

For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team.

You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done.

Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems.

Please sign up here! https://t.co/0rObe4feBz

[Kris @AllAbtAI]

Interesting 💯

[Stack Overflow @StackOverflow]

We’re thrilled to announce we’re partnering with @OpenAI to bring best in class technical knowledge and the world’s most popular LLM models for AI development together! This groundbreaking partnership with OpenAI will drive our mission to empower the world to develop technology through collective knowledge.

https://t.co/CbgZIEoB2z

[Kris @AllAbtAI]

pretty cool 🤩

[OpenAI @OpenAI]

A glimpse of our early work with artists and filmmakers to see how Sora can help bring ideas into reality: https://t.co/kIowcqry0d

[OpenAI @OpenAI]

A glimpse of our early work with artists and filmmakers to see how Sora can help bring ideas into reality: https://t.co/kIowcqry0d

[Dave Diamond @mrdavediamond]

Another killer video from @AllAbtAI on getting LLMs to "Think before they speak"

[Kris @AllAbtAI]

I have been testing the Claude 3 Opus API today, and gotta say I really really like it so far🔥

It solved my custom Lateral Thinking Puzzle with ease. More testing to do, but well done @AnthropicAI 👏

#ai #claude3 #Anthropic #tech #llm #GenAI

[Kris @AllAbtAI]

I have tested the @GroqInc API for different tasks like:
✅ Real Time Speech to Speech
✅ Groq vs ChatGPT Speed
✅ Chain Prompting
Really impressed so far! More tests do to soon🤖

#ai #LLM #tech #aiengineer #SoftwareEngineer

https://t.co/BvyEBqJheG

[Kris @AllAbtAI]

Insane inference using the @GroqInc API🔥

I made a small counter that showed over 200 tokens/s (not 100% accurate but pretty close)

VERY excited about this. More in Sundays YT video 🤖

#groq #ai #llm #tech #aiengineer

[Kris @AllAbtAI]

OpenAI Sora🤯 my first impressions
#OpenAI #sora #openaisora #tech #texttovideo

https://t.co/z5xUPC2kuh

[Kris @AllAbtAI]

A 1M+ context window from Gemini 1.5 got my attension more than Ultra 1.0 👀

https://t.co/PMjiAM2cFC

[Kris @AllAbtAI]

My Contribution to the Nvidia Gen AI RTX Contest 🔥 A simple Speech to Image App powered by SDXL TensorRT and Faster-whisper! @NVIDIAAIDev
#GenAIonRTX #DevContest #GTC24 #Nvidia #ai #tech #stablediffusion