5 December 2025
Ai Top News
AWS doubles down on custom LLMs with features meant to simplify model creation
Tl;dr: AWS announced new capabilities in Amazon Bedrock and SageMaker AI to make building and fine-tuning custom large language models easier for developers.
Key Takeaways:
- AWS introduced serverless model customization in SageMaker for customizing Amazon's own Nova models and open-source models.
- Reinforcement Fine-Tuning in Bedrock allows developers to choose a reward function or workflow for automatic model customization.
- These new capabilities are designed to help enterprise customers create unique solutions optimized for their brand and use case.
Yesterday
aws.amazon.com
Amazon Bedrock adds reinforcement fine-tuning simplifying how developers build smarter, more accurate AI models
Tl;dr: Amazon Bedrock introduces reinforcement fine-tuning, making advanced model customization accessible to developers without requiring deep ML expertise. This feature improves model accuracy by 66% on average over base models and reduces costs.
Key Takeaways:
- Amazon Bedrock now offers reinforcement fine-tuning, an advanced technique that trains models using feedback instead of massive labeled datasets, eliminating the need for ML expertise.
- The feature improves model accuracy by 66% on average over base models and reduces costs by allowing developers to use smaller, faster, and more efficient model variants.
- Reinforcement fine-tuning in Amazon Bedrock automates the workflow, making it easier for developers to build smarter, more cost-effective AI models.
- Amazon Bedrock supports two approaches to reinforcement fine-tuning: Reinforcement Learning with Verifiable Rewards (RLVR) and Reinforcement Learning from AI Feedback (RLAIF).
Yesterday
aws.amazon.com
New serverless customization in Amazon SageMaker AI accelerates model fine-tuning
Tl;dr: Amazon SageMaker AI now enables serverless model fine-tuning with popular AI models, reducing the process from months to days through easy-to-use interface and auto-provisioning of compute resources.
Key Takeaways:
- Amazon SageMaker AI supports serverless model fine-tuning with AI models like Nova, DeepSeek, GPT-OSS, Llama, and Qwen, accelerating the process from months to days.
- The service provides an easy-to-use interface for fine-tuning techniques like reinforcement learning and allows seamless model evaluation and deployment without managing infrastructure.
- Serverless MLflow capability enables logging of critical experiment metrics without modifying code and access to rich visualizations for further analysis.
- Amazon SageMaker AI is available in US East, US West, Asia Pacific, and Europe Regions with a pay-as-you-go pricing model based on the tokens processed during training and inference.
Yesterday
Acquisitions
OpenAI acquires "Neptune" to improve AI model training,Rivals(Samsung) will lose access in months
OpenAI has agreed to acquire **Neptune, a Polish startup** that builds tooling to analyze and debug AI training runs.
Neptune helps teams track experiments, compare models and spot failures during training. OpenAI has **already** been using it **internally** for over a year.
The most aggressive part of the deal is that Neptune will **wind down external access** for all other customers (which reportedly included Samsung, HP and Poolside) to focus 100% on OpenAI.
Jakub Pachocki (OpenAI) said the acquisition will **expand visibility into how models learn.**
**Source: Bloomberg**
🔗 : https://www.bloomberg.com/news/articles/2025-12-03/openai-agrees-to-acquire-neptune-to-improve-ai-model-training
--- TOP COMMENTS ---
I use them myself for model training. I don't understand why they can't continue to operate after the acquisition like anthropic and bun
---
Talk about a desperate business model.
Own visibility for supression and data theft of other competitors.
Don't let the logs get exposed via third party.
Erase and deny everything ..
Neptune helps teams track experiments, compare models and spot failures during training. OpenAI has **already** been using it **internally** for over a year.
The most aggressive part of the deal is that Neptune will **wind down external access** for all other customers (which reportedly included Samsung, HP and Poolside) to focus 100% on OpenAI.
Jakub Pachocki (OpenAI) said the acquisition will **expand visibility into how models learn.**
**Source: Bloomberg**
🔗 : https://www.bloomberg.com/news/articles/2025-12-03/openai-agrees-to-acquire-neptune-to-improve-ai-model-training
--- TOP COMMENTS ---
I use them myself for model training. I don't understand why they can't continue to operate after the acquisition like anthropic and bun
---
Talk about a desperate business model.
Own visibility for supression and data theft of other competitors.
Don't let the logs get exposed via third party.
Erase and deny everything ..
Yesterday
Companies
There's only 3 main companies that make consumer RAM chips and one of them just bounced for AI
Micron, a major manufacturer of RAM chips, is exiting the consumer memory business (**Crucial**) and shifting hard toward memory for AI/data center workloads instead. This is happening right in the middle of a global memory crunch driven by AI data centers hoovering up DRAM and HBM, and it’s not great news for regular PC builders.
GPU prices went to the moon first, now RAM is lining up next as more supply gets redirected to servers and high-margin AI stuff instead of consumer kits. If you were planning a build or an upgrade, this might be the last reasonably sane window before Crucial disappears from the consumer market over the next year or so.
[Official Micron Press Release](https://investors.micron.com/news-releases/news-release-details/micron-announces-exit-crucial-consumer-business)
--- TOP COMMENTS ---
Micron dropping Crucial means less consumer RAM and higher prices as AI eats supply.
---
Right now AI data centers are paying pretty much name your price to companies building those daracenters. Whose money are they paying with, no one knows. They just assume that the money will be there in the future when the bill comes due.
PC makers don’t have the same inflated money balloon...
GPU prices went to the moon first, now RAM is lining up next as more supply gets redirected to servers and high-margin AI stuff instead of consumer kits. If you were planning a build or an upgrade, this might be the last reasonably sane window before Crucial disappears from the consumer market over the next year or so.
[Official Micron Press Release](https://investors.micron.com/news-releases/news-release-details/micron-announces-exit-crucial-consumer-business)
--- TOP COMMENTS ---
Micron dropping Crucial means less consumer RAM and higher prices as AI eats supply.
---
Right now AI data centers are paying pretty much name your price to companies building those daracenters. Whose money are they paying with, no one knows. They just assume that the money will be there in the future when the bill comes due.
PC makers don’t have the same inflated money balloon...
Yesterday
Micron is killing Crucial SSD and DRAM.
This is being done to put all it's production for AI and Datacenters. I don't think the RAM prices are going down anytime soon...
--- TOP COMMENTS ---
Honestly the last half decade has been terrible in terms of quality offerings of PC hardware. With nVidia's crazy price raises, lack of competition, and the exit of some of the rare decent options in terms of how they deal with customers (E.G. EVGA), it's gone from hard to be interested in PC gaming more than casually to utterly brutal. They've turned what was already an expensive hobby into a hobby for the rich.
---
I am not gonna lie fuck those bastards
Edit: since some of you guys love these monopol companies and support the "free" market that they are in i dont see a reason to have a conversation with you. Please feel free to contact me when you realize there is more to life aside from profits, supplies and demands. From my point of view you guys are asking for technofeodality...
--- TOP COMMENTS ---
Honestly the last half decade has been terrible in terms of quality offerings of PC hardware. With nVidia's crazy price raises, lack of competition, and the exit of some of the rare decent options in terms of how they deal with customers (E.G. EVGA), it's gone from hard to be interested in PC gaming more than casually to utterly brutal. They've turned what was already an expensive hobby into a hobby for the rich.
---
I am not gonna lie fuck those bastards
Edit: since some of you guys love these monopol companies and support the "free" market that they are in i dont see a reason to have a conversation with you. Please feel free to contact me when you realize there is more to life aside from profits, supplies and demands. From my point of view you guys are asking for technofeodality...
Yesterday
Anthropic CEO Dario Amodei says competitors are "YOLO-ing" capital and taking too much risk. (Bloomberg Interview)
**Image: Anthropic CEO Dario during New York Times DealBook summit on Dec-3**
**The Quote:** Amodei said, **There are some players who are YOLO-ing... and pull the risk dial too far,** referring to the massive capital burn in the industry.
**Anthropic's Bet:** Committed **$50 Billion** for US data centers (Texas/NY) to focus on "responsible scaling".
**The Competitor (Implied):** OpenAI has reportedly committed up to **$1.4 Trillion** in infrastructure deals.
**The Dilemma:** He highlights the risk of building multi-year **Gigawatt** data centers when the economic value of AI models is still uncertain.
It seems Anthropic is positioning itself as the **Capital Efficient** lab vs the **Brute Force** labs. *YOLO - You Only Live Once*
**Source: Bloomberg**
🔗 : https://www.bloomberg.com/news/articles/2025-12-03/anthropic-ceo-says-some-tech-firms-too-risky-with-ai-spending?embedded-checkout=true
--- TOP COMMENTS ---
>Anthropic CEO Dario Amodei says competitors are "YOLO-ing" capital and taking too much risk.
Out of all the big AI companies, Open AI, Google, Microsoft, X, Anthropic has the lowest amount of funding by far. So, if you're Anthropic, and investors and the public see your lack of funding, they are going to think Anthropic isn't worth much. If you are Dario, saying "Yeah, they are just wasting money, spending it recklessly" is the best way to spin the difference in capital between your company and theirs.
---
They're also the ones closest to profitability, right? It seems like if there's a right way to deal with this impending bubble, Anthropic is the only one doing it...
**The Quote:** Amodei said, **There are some players who are YOLO-ing... and pull the risk dial too far,** referring to the massive capital burn in the industry.
**Anthropic's Bet:** Committed **$50 Billion** for US data centers (Texas/NY) to focus on "responsible scaling".
**The Competitor (Implied):** OpenAI has reportedly committed up to **$1.4 Trillion** in infrastructure deals.
**The Dilemma:** He highlights the risk of building multi-year **Gigawatt** data centers when the economic value of AI models is still uncertain.
It seems Anthropic is positioning itself as the **Capital Efficient** lab vs the **Brute Force** labs. *YOLO - You Only Live Once*
**Source: Bloomberg**
🔗 : https://www.bloomberg.com/news/articles/2025-12-03/anthropic-ceo-says-some-tech-firms-too-risky-with-ai-spending?embedded-checkout=true
--- TOP COMMENTS ---
>Anthropic CEO Dario Amodei says competitors are "YOLO-ing" capital and taking too much risk.
Out of all the big AI companies, Open AI, Google, Microsoft, X, Anthropic has the lowest amount of funding by far. So, if you're Anthropic, and investors and the public see your lack of funding, they are going to think Anthropic isn't worth much. If you are Dario, saying "Yeah, they are just wasting money, spending it recklessly" is the best way to spin the difference in capital between your company and theirs.
---
They're also the ones closest to profitability, right? It seems like if there's a right way to deal with this impending bubble, Anthropic is the only one doing it...
Yesterday
OpenAI's moat didn't leak, three forces broke it at once
Everyone's framing the past two weeks as OpenAI vs Google.
ChatGPT DAUs down 6% after Gemini 3 launched. Sam Altman declares "Code Red" internally. Pauses ads, shopping, health agents. All hands on deck. But i think that's not the story. The story is that three forces converged in the same month, and OpenAI can't outrun all of them at once.
**Force 1: China's november**
Chinese labs shipped 15 open-weight models in November. Not research previews. Production-ready, MIT-licensed models.
**The headline:** Moonshot AI's Kimi K2 Thinking. 1 trillion parameters, 32 billion active. Scores 67 on Artificial Analysis. For context, GPT-5 medium scores 66.
All that with quite a price difference:
\- GPT-5 medium: $3.44/M tokens
\- Kimi K2 Thinking: $1.07/M tokens
\- DeepSeek V3.2: $0.32/M tokens
for the same capability tier, 10x spread. All the cheap ones are now open-weight.
November also brought VibeThinker (beats DeepSeek-R1 on AIME math), Step-Audio-R1 (first open audio reasoning model), HunyuanVideo 1.5 (8.3B video gen), and a half-dozen others. The pace didn't slow down for a single week.
**Force 2: efficiency ate scale**
Two years ago, bigger meant better. The compute barrier was the moat, but not anymore. OpenAI's own leaked model proves it. "Garlic" is smaller than their flagships but reportedly beats GPT-4.5 on coding and reasoning. They're targeting "big-model intelligence in smaller architectures." Expected as GPT-5.2 or 5.5 early 2026.
Mistral dropped 10 open-weight models yesterday. The flagship is a 675B MoE. But the real story is Ministral 3B. It runs entirely in your browser via WebGPU. No server. No API call. No cloud bill. Three billion parameters, SOTA for its size class, running on your laptop. The paradigm flipped, efficient is the new big.
**Force 3: silicon broke free**
Amazon's Trainium3 launched December 2. 3nm chip, 4x faster than Trainium2, 40% more energy efficient. Anthropic is already using it for Claude. The bigger news: Trainium4 will support NVIDIA's NVLink Fusion. You'll be able to mix AWS silicon with NVIDIA hardware in the same cluster. The CUDA lock-in that kept everyone on NVIDIA is cracking.
Same day, Tether released QVAC Fabric. First production-ready framework for fine-tuning LLMs on consumer GPUs and mobile devices. Qualcomm Adreno, ARM Mali, Apple Silicon. Apache 2.0. Cloud inference costs used to be the barrier to entry. That barrier is falling.
**bottom line**
OpenAI's panic isn't about Gemini beating ChatGPT on some benchmark. It's about the simultaneous arrival of:
\- Open-weight models that match proprietary at 1/10th the price
\- Efficient architectures that obsolete scale advantages
\- Silicon that breaks cloud monopolies
The moat didn't spring a leak. Three forces broke it at once. And no single company can outrun physics.
Wrote up the full analysis with data tables and more context: [https://www.whatllm.org/blog/three-forces-broke-openai-moat](https://www.whatllm.org/blog/three-forces-broke-openai-moat)
--- TOP COMMENTS ---
Chinese open source is a really big factor. It sets a price cap for a given capability. You either need to be much better in a niche (e.g. Claude for coding) or you will be under competitive pressure. This is nothing new for Chinese businesses, they are cut-throat on price and have to find other ways to differentiate.
It also calls into question OpenAIs business model, how will they monetize, can they generate enough profits to justify massive infra build-out?
---
what is happening with this sub.....
ChatGPT DAUs down 6% after Gemini 3 launched. Sam Altman declares "Code Red" internally. Pauses ads, shopping, health agents. All hands on deck. But i think that's not the story. The story is that three forces converged in the same month, and OpenAI can't outrun all of them at once.
**Force 1: China's november**
Chinese labs shipped 15 open-weight models in November. Not research previews. Production-ready, MIT-licensed models.
**The headline:** Moonshot AI's Kimi K2 Thinking. 1 trillion parameters, 32 billion active. Scores 67 on Artificial Analysis. For context, GPT-5 medium scores 66.
All that with quite a price difference:
\- GPT-5 medium: $3.44/M tokens
\- Kimi K2 Thinking: $1.07/M tokens
\- DeepSeek V3.2: $0.32/M tokens
for the same capability tier, 10x spread. All the cheap ones are now open-weight.
November also brought VibeThinker (beats DeepSeek-R1 on AIME math), Step-Audio-R1 (first open audio reasoning model), HunyuanVideo 1.5 (8.3B video gen), and a half-dozen others. The pace didn't slow down for a single week.
**Force 2: efficiency ate scale**
Two years ago, bigger meant better. The compute barrier was the moat, but not anymore. OpenAI's own leaked model proves it. "Garlic" is smaller than their flagships but reportedly beats GPT-4.5 on coding and reasoning. They're targeting "big-model intelligence in smaller architectures." Expected as GPT-5.2 or 5.5 early 2026.
Mistral dropped 10 open-weight models yesterday. The flagship is a 675B MoE. But the real story is Ministral 3B. It runs entirely in your browser via WebGPU. No server. No API call. No cloud bill. Three billion parameters, SOTA for its size class, running on your laptop. The paradigm flipped, efficient is the new big.
**Force 3: silicon broke free**
Amazon's Trainium3 launched December 2. 3nm chip, 4x faster than Trainium2, 40% more energy efficient. Anthropic is already using it for Claude. The bigger news: Trainium4 will support NVIDIA's NVLink Fusion. You'll be able to mix AWS silicon with NVIDIA hardware in the same cluster. The CUDA lock-in that kept everyone on NVIDIA is cracking.
Same day, Tether released QVAC Fabric. First production-ready framework for fine-tuning LLMs on consumer GPUs and mobile devices. Qualcomm Adreno, ARM Mali, Apple Silicon. Apache 2.0. Cloud inference costs used to be the barrier to entry. That barrier is falling.
**bottom line**
OpenAI's panic isn't about Gemini beating ChatGPT on some benchmark. It's about the simultaneous arrival of:
\- Open-weight models that match proprietary at 1/10th the price
\- Efficient architectures that obsolete scale advantages
\- Silicon that breaks cloud monopolies
The moat didn't spring a leak. Three forces broke it at once. And no single company can outrun physics.
Wrote up the full analysis with data tables and more context: [https://www.whatllm.org/blog/three-forces-broke-openai-moat](https://www.whatllm.org/blog/three-forces-broke-openai-moat)
--- TOP COMMENTS ---
Chinese open source is a really big factor. It sets a price cap for a given capability. You either need to be much better in a niche (e.g. Claude for coding) or you will be under competitive pressure. This is nothing new for Chinese businesses, they are cut-throat on price and have to find other ways to differentiate.
It also calls into question OpenAIs business model, how will they monetize, can they generate enough profits to justify massive infra build-out?
---
what is happening with this sub.....
Yesterday
IBM CEO says there is 'no way' spending trillions on AI data centers will pay off at today's infrastructure costs
* IBM's CEO walked through some napkin math on data centers— and said that there's "no way" to turn a profit at current costs.
* "$8 trillion of CapEx means you need roughly $800 billion of profit just to pay for the interest," Arvind Krishna told "Decoder."
* Krishna was skeptical of that current tech would reach AGI, putting the likelihood between 0-1%.
[Source](https://www.businessinsider.com/ibm-ceo-big-tech-ai-capex-data-center-spending-2025-12)
--- TOP COMMENTS ---
Your human brain uses about 20 watts when you’re thinking.
Your phone uses about this much energy when running a graphically intensive game.
If they’re having to build gigantic data centers with billions of dollars of GPUs in the search for AGI, I feel that they’re on completely the wrong path and are building the wrong hardware for the wrong software.
If AGI comes to consumers in a few years - and it is running on your phone with a next generation AI chip - it’s going to make a lot of the current data centers look like a completely obsolete waste of money.
---
He's making sense to me. Returns from scaling has apparently plateaued already. They can't do the complex stuff reliably, so their future seems more like the scaled back Chinese models intended to achieve far less ambitious goals, which can even run on self-hosted servers.
The assumption that scale will lead to AGI seems already debunked, and this scepticism is fast spreading, and the failure of GPT5 to be exponentially better than 4 is being cited as strong evidence that LLMs can only make incremental improvements from here.
The big announcements seem like inertia. Concurrent with that is doubt and re-examination of the benefits of scaling further... I suspect there will be a bunch of cancellations of DC plans, with maybe only Elon still saying we need to 10x again, cause he's the biggest monorail salesman in Springfield...
* "$8 trillion of CapEx means you need roughly $800 billion of profit just to pay for the interest," Arvind Krishna told "Decoder."
* Krishna was skeptical of that current tech would reach AGI, putting the likelihood between 0-1%.
[Source](https://www.businessinsider.com/ibm-ceo-big-tech-ai-capex-data-center-spending-2025-12)
--- TOP COMMENTS ---
Your human brain uses about 20 watts when you’re thinking.
Your phone uses about this much energy when running a graphically intensive game.
If they’re having to build gigantic data centers with billions of dollars of GPUs in the search for AGI, I feel that they’re on completely the wrong path and are building the wrong hardware for the wrong software.
If AGI comes to consumers in a few years - and it is running on your phone with a next generation AI chip - it’s going to make a lot of the current data centers look like a completely obsolete waste of money.
---
He's making sense to me. Returns from scaling has apparently plateaued already. They can't do the complex stuff reliably, so their future seems more like the scaled back Chinese models intended to achieve far less ambitious goals, which can even run on self-hosted servers.
The assumption that scale will lead to AGI seems already debunked, and this scepticism is fast spreading, and the failure of GPT5 to be exponentially better than 4 is being cited as strong evidence that LLMs can only make incremental improvements from here.
The big announcements seem like inertia. Concurrent with that is doubt and re-examination of the benefits of scaling further... I suspect there will be a bunch of cancellations of DC plans, with maybe only Elon still saying we need to 10x again, cause he's the biggest monorail salesman in Springfield...
Yesterday
Anthropic going public and private user data security better be sustained
With the recent news of Anthropic going public within the next couple of months or more, I really hope that they keep their promise and vision on not selling users data and keeping everyone’s data private. Besides Claude being the best LLM IMO and also tons of other people who share the same opinion because it’s true, the other top reason why I only started using Anthropic LLMs last year was because of privacy and security. I only use Claude and I use it all the time, on my phone, desktop app with MPC, Claude Code some, and website. I like privacy, knowing that my usage isn’t being sold, looked through, used to be researched and used for other power of if I am getting good products and usage out of it to further my own development. Say I come up with something really useful, I don’t want say OpenAI or other companies like Google with their Gemini taking my info and using it for whatever. I’m sure that’s what they are doing, monitoring their users and if they come up with good things they copy it or just your ideas and such aren’t yours. It’s like sharing ideas with friends or people around in any fashion, but you either hint at something or don’t tell them what you’re doing or thinking, then them going off and stealing your idea, or a startup failing because someone else somehow knew your idea or product as you started working on it.
My ideas are mine and regardless if I build it with a LLM from Anthropic or any other LLM I feel like it’s still the users info and IP if it’s not something that was already IP or owned by someone else out on the internet if that info was taken from there. I can code and all, come up with ideas, make products or anything else. The only thing is time, effort, and execution.
I can see shareholders wanting to sell users data, data scrape it, sell their ideas and products, thoughts, personal information, health information, life stories, data from research, math equations or new ways of doing things, breakthroughs, etc. I for one would hate this to happen. The markets are honestly one of the best things when used correctly, but also one of the worst things. I seriously don’t like or believe in all this data sharing between everyone and these companies that are for insane profits, feels like your data regardless of what it is, isn’t yours. So many people share things on the web for free and Idk why a lot of the times. It’s like Microsoft thinking they have a good OS, when in reality they just shoved themselves into a market to have a chance and it stuck, even though it’s terrible.
TL;DR if you’re one of those people: When or if Anthropic goes public on the stock exchange all I care for is the usages and more importantly the current privacy and MOTO to be maintained. If that isn’t kept then Idk what I’ll do. I’ll still use it but I’ll be real pissed. I’ll just have to get all what I’m trying to do before that day comes and delete my chats. I’ve lived by the saying somewhat the last few years, “Do everything and realize as much as you can as soon as possible.” This way you take advantage of things and not have issues down the line for whatever happens in life. Go first.
--- TOP COMMENTS ---
In any case, if your data is stored on a US server, there is no reason to believe that it is safe.
The problem with most APIs/subscriptions is the total lack of clarity that service providers maintain regarding confidentiality. What security measures are in place to prevent criminal data leaks? What long-term commitments are needed, as you point out? Most tech sectors work closely with the government and federal agencies, they are subject to the same financial pressures than other tech companies... It is not without reason that, for example, European public research strongly advises scientists against using foreign services. Even EU servers are susceptible to the US Cloud Act. And I'm not even talking about China... (which at least has the merit of offering us solutions to run locally/VM). I handle sensitive data (in-depth interviews with individuals, life stories, political opinions, etc.). It's impossible for me to trust anything other than a local solution or a national cloud like OVH (and even then...) or the Mistral API (Wait... Mistral mostly uses US servers, lol).
I admit that it's generally frustrating compared to Anthropic's models.
Shareholders are just one of the problems with these services. It's the entire globalized American economic system that calls for caution. We've clearly seen that the right to confidentiality is the first security lock to be broken in our liberal democracies when it suits certain sectors of the administration and economy. Diplomacy can do nothing, even the leaders are being wiretapped and the only thing they can think to do is back down (Merkel and Hollande, at the time).
I recently watched a documentary about how the CIA, through its IQT investment fund, had carried out a takeover bid for GemPlus, the pioneering French smart card company. Opaque, unfair practices (the fact that the CEO of GemPlus wasn't very intelligent helped)... In order to have access to and management of the encryption keys, telecommunications networks. I have no reason to believe that all of this has changed in 25 years : There will always be an incentive to make this data available to the American government and economic actors: whether it is for product improvement (competitiveness) or threat management (internal security and surveillance).
It makes me sound like a doomer, a conspiracy theorist, a pessimist... But I ultimately find that the dystopias of the 20th century were very insightful about the risks of modern information-oriented societies.
---
You have no way of knowing what they are really doing with your data, you just have to trust them. Unless you run opensource AIs on your own machine and then disconnect them from the Internet, you really have no idea what is going on with your data...
My ideas are mine and regardless if I build it with a LLM from Anthropic or any other LLM I feel like it’s still the users info and IP if it’s not something that was already IP or owned by someone else out on the internet if that info was taken from there. I can code and all, come up with ideas, make products or anything else. The only thing is time, effort, and execution.
I can see shareholders wanting to sell users data, data scrape it, sell their ideas and products, thoughts, personal information, health information, life stories, data from research, math equations or new ways of doing things, breakthroughs, etc. I for one would hate this to happen. The markets are honestly one of the best things when used correctly, but also one of the worst things. I seriously don’t like or believe in all this data sharing between everyone and these companies that are for insane profits, feels like your data regardless of what it is, isn’t yours. So many people share things on the web for free and Idk why a lot of the times. It’s like Microsoft thinking they have a good OS, when in reality they just shoved themselves into a market to have a chance and it stuck, even though it’s terrible.
TL;DR if you’re one of those people: When or if Anthropic goes public on the stock exchange all I care for is the usages and more importantly the current privacy and MOTO to be maintained. If that isn’t kept then Idk what I’ll do. I’ll still use it but I’ll be real pissed. I’ll just have to get all what I’m trying to do before that day comes and delete my chats. I’ve lived by the saying somewhat the last few years, “Do everything and realize as much as you can as soon as possible.” This way you take advantage of things and not have issues down the line for whatever happens in life. Go first.
--- TOP COMMENTS ---
In any case, if your data is stored on a US server, there is no reason to believe that it is safe.
The problem with most APIs/subscriptions is the total lack of clarity that service providers maintain regarding confidentiality. What security measures are in place to prevent criminal data leaks? What long-term commitments are needed, as you point out? Most tech sectors work closely with the government and federal agencies, they are subject to the same financial pressures than other tech companies... It is not without reason that, for example, European public research strongly advises scientists against using foreign services. Even EU servers are susceptible to the US Cloud Act. And I'm not even talking about China... (which at least has the merit of offering us solutions to run locally/VM). I handle sensitive data (in-depth interviews with individuals, life stories, political opinions, etc.). It's impossible for me to trust anything other than a local solution or a national cloud like OVH (and even then...) or the Mistral API (Wait... Mistral mostly uses US servers, lol).
I admit that it's generally frustrating compared to Anthropic's models.
Shareholders are just one of the problems with these services. It's the entire globalized American economic system that calls for caution. We've clearly seen that the right to confidentiality is the first security lock to be broken in our liberal democracies when it suits certain sectors of the administration and economy. Diplomacy can do nothing, even the leaders are being wiretapped and the only thing they can think to do is back down (Merkel and Hollande, at the time).
I recently watched a documentary about how the CIA, through its IQT investment fund, had carried out a takeover bid for GemPlus, the pioneering French smart card company. Opaque, unfair practices (the fact that the CEO of GemPlus wasn't very intelligent helped)... In order to have access to and management of the encryption keys, telecommunications networks. I have no reason to believe that all of this has changed in 25 years : There will always be an incentive to make this data available to the American government and economic actors: whether it is for product improvement (competitiveness) or threat management (internal security and surveillance).
It makes me sound like a doomer, a conspiracy theorist, a pessimist... But I ultimately find that the dystopias of the 20th century were very insightful about the risks of modern information-oriented societies.
---
You have no way of knowing what they are really doing with your data, you just have to trust them. Unless you run opensource AIs on your own machine and then disconnect them from the Internet, you really have no idea what is going on with your data...
Yesterday
This is why OpenAI is in a Code Red
--- TOP COMMENTS ---
This is a short time frame that also overlaps with the Thanksgiving holiday in the US (which is still within the chart's current 7-day rolling average). Many people had multiple days off from work and school during that time.
I wonder how much of the dip is because of that.
---
OpenAI has to continue raising 10s to 100s of billions. Every chart has to go up and to the right, otherwise raising this much becomes much more difficult. It's a high stakes game of growth at all costs that has to continue for years...
Yesterday
OpenAI just hit code red. Three years after Google panicked over ChatGPT. Now the roles flipped.
So yesterday Sam Altman sent an internal memo to everyone at OpenAI. Code red. Everything stops. Fix ChatGPT. All resources on quality.
And I mean everything stopped. The ads they were about to launch? Delayed. AI shopping stuff? On hold. Health agents? Nope. Pulse their personal assistant thing? Indefinitely delayed.
In December 2022, about three years ago, Google treated ChatGPT as a serious problem. Sundar asked Larry and Sergey to return, and a lot of teams kept working through the Christmas period because of it.
Now it's OpenAI panicking over Gemini. The roles literally reversed.
November 21, Google dropped Gemini 3. Topped the benchmarks. Beat GPT-5. Then Marc Benioff the Salesforce CEO posted he used ChatGPT every day for 3 years. Tried Gemini 3 for 2 hours. Said he's not going back. One month after Salesforce signed a $100 million deal with OpenAI. One month.
Then Anthropic dropped Claude Opus 4.5 last week. Also beating GPT-5. ChatGPT's not the king anymore and OpenAI's losing it.
They're bleeding cash. $14 billion in losses projected by 2026. Making $20 billion revenue but spending way more. Raised $6.6 billion in October at $157 billion valuation and that valuation assumes they're THE leader. If they start losing users that number looks stupid.
ChatGPT has 800 million weekly users. Gemini hit 650 million monthly. That gap's closing stupid fast.
And the product's been a mess. They tightened safety stuff. Users said it got boring. So they loosened it. Added erotica for verified adults. Tried to bring personality back. Still not connecting. Growth slowed. October they had code orange. Now it's full red.
Altman's saying new reasoning model next week beats Gemini 3 in their tests. ChatGPT's getting faster more reliable better at personalization. But they've been saying this for months. GPT-5 dropped in August and people were like meh. Not the leap they wanted. Now they're scrambling.
The irony kills me. Remember when OpenAI caught Google sleeping? Google had LaMDA ready. Didn't launch because worried about reputation. ChatGPT dropped. Went viral. Google panicked. Rushed out Bard, February 2023. First demo had a wrong space answer. Stock tanked $100 billion in one day. Now it's OpenAI getting caught. Got comfortable. Gemini 3 launched. Now they're behind. Same exact pattern.
But the real problem is, Ilya Sutskever, OpenAI co-founder now at his own company said it out loud. 2020 to 2025 was the age of scaling. Just add more compute. But now the scale is so big. You think if you 100x it everything transforms? He doesn't think that's true.
They're hitting a wall. Can't just spend more money and get better results anymore. Yann LeCun from Meta agrees. Says we're not getting to human level AI by scaling up LLMs. It's not happening.
So OpenAI's whole strategy was spend more build bigger. Now that doesn't work. That's the panic. Their advantage was scale. Scale isn't enough.
They were gonna launch ads. Engineer found code in Android app last week. Now delayed. Altman once said ads plus AI is uniquely unsettling but they need money. Not profitable. Ads were the plan. Can't even launch that because ChatGPT's not good enough.
And they're bleeding talent. Mira Murati former CTO started Thinking Machines. Took 20+ OpenAI people. Alexandr Wang went to Meta's Superintelligence thing. So they're declaring code red while losing their best people.
Altman says new model next week beats Gemini 3. But Google and Anthropic aren't waiting. They'll drop updates too. This is the reality now. No sustained leader. Release newest model. Win for a few weeks. Someone else releases. Repeat.
OpenAI thought they'd stay ahead. Got comfortable. Now playing catch up. And they've got $1.4 trillion in infrastructure commitments. Need growth to afford that. User growth stalls? Valuation drops. Can't raise more money. Can't meet commitments. That's why it's code red. Not just competitive. Existential.
**TLDR:** Altman sent memo yesterday code red. Delaying everything ads agents health shopping Pulse. All resources fixing ChatGPT. Gemini 3 and Claude beating GPT-5. Benioff ditched ChatGPT for Gemini month after $100M OpenAI deal. Three years ago Google panicked over ChatGPT now reversed. Losing billions $157B valuation assumes they're leader. Gemini 650M users ChatGPT 800M gap closing. Ilya said age of scaling over. Yann LeCun agrees. Bleeding top people. $1.4T commitments need growth. Existential not just competitive.
**Sources:**
[https://fortune.com/2025/12/02/sam-altman-declares-code-red-google-gemini-ceo-sundar-pichai/](https://fortune.com/2025/12/02/sam-altman-declares-code-red-google-gemini-ceo-sundar-pichai/)
--- TOP COMMENTS ---
You seem giddy over this. It’s certainly interesting but the race is not over. Google has made massive strides, no doubt. For the sake of us as users, we need healthy competition. I’m a Google investor and wish the best for them but this isn’t over yet.
---
Sounds like healthy competition. Good for development...
And I mean everything stopped. The ads they were about to launch? Delayed. AI shopping stuff? On hold. Health agents? Nope. Pulse their personal assistant thing? Indefinitely delayed.
In December 2022, about three years ago, Google treated ChatGPT as a serious problem. Sundar asked Larry and Sergey to return, and a lot of teams kept working through the Christmas period because of it.
Now it's OpenAI panicking over Gemini. The roles literally reversed.
November 21, Google dropped Gemini 3. Topped the benchmarks. Beat GPT-5. Then Marc Benioff the Salesforce CEO posted he used ChatGPT every day for 3 years. Tried Gemini 3 for 2 hours. Said he's not going back. One month after Salesforce signed a $100 million deal with OpenAI. One month.
Then Anthropic dropped Claude Opus 4.5 last week. Also beating GPT-5. ChatGPT's not the king anymore and OpenAI's losing it.
They're bleeding cash. $14 billion in losses projected by 2026. Making $20 billion revenue but spending way more. Raised $6.6 billion in October at $157 billion valuation and that valuation assumes they're THE leader. If they start losing users that number looks stupid.
ChatGPT has 800 million weekly users. Gemini hit 650 million monthly. That gap's closing stupid fast.
And the product's been a mess. They tightened safety stuff. Users said it got boring. So they loosened it. Added erotica for verified adults. Tried to bring personality back. Still not connecting. Growth slowed. October they had code orange. Now it's full red.
Altman's saying new reasoning model next week beats Gemini 3 in their tests. ChatGPT's getting faster more reliable better at personalization. But they've been saying this for months. GPT-5 dropped in August and people were like meh. Not the leap they wanted. Now they're scrambling.
The irony kills me. Remember when OpenAI caught Google sleeping? Google had LaMDA ready. Didn't launch because worried about reputation. ChatGPT dropped. Went viral. Google panicked. Rushed out Bard, February 2023. First demo had a wrong space answer. Stock tanked $100 billion in one day. Now it's OpenAI getting caught. Got comfortable. Gemini 3 launched. Now they're behind. Same exact pattern.
But the real problem is, Ilya Sutskever, OpenAI co-founder now at his own company said it out loud. 2020 to 2025 was the age of scaling. Just add more compute. But now the scale is so big. You think if you 100x it everything transforms? He doesn't think that's true.
They're hitting a wall. Can't just spend more money and get better results anymore. Yann LeCun from Meta agrees. Says we're not getting to human level AI by scaling up LLMs. It's not happening.
So OpenAI's whole strategy was spend more build bigger. Now that doesn't work. That's the panic. Their advantage was scale. Scale isn't enough.
They were gonna launch ads. Engineer found code in Android app last week. Now delayed. Altman once said ads plus AI is uniquely unsettling but they need money. Not profitable. Ads were the plan. Can't even launch that because ChatGPT's not good enough.
And they're bleeding talent. Mira Murati former CTO started Thinking Machines. Took 20+ OpenAI people. Alexandr Wang went to Meta's Superintelligence thing. So they're declaring code red while losing their best people.
Altman says new model next week beats Gemini 3. But Google and Anthropic aren't waiting. They'll drop updates too. This is the reality now. No sustained leader. Release newest model. Win for a few weeks. Someone else releases. Repeat.
OpenAI thought they'd stay ahead. Got comfortable. Now playing catch up. And they've got $1.4 trillion in infrastructure commitments. Need growth to afford that. User growth stalls? Valuation drops. Can't raise more money. Can't meet commitments. That's why it's code red. Not just competitive. Existential.
**TLDR:** Altman sent memo yesterday code red. Delaying everything ads agents health shopping Pulse. All resources fixing ChatGPT. Gemini 3 and Claude beating GPT-5. Benioff ditched ChatGPT for Gemini month after $100M OpenAI deal. Three years ago Google panicked over ChatGPT now reversed. Losing billions $157B valuation assumes they're leader. Gemini 650M users ChatGPT 800M gap closing. Ilya said age of scaling over. Yann LeCun agrees. Bleeding top people. $1.4T commitments need growth. Existential not just competitive.
**Sources:**
[https://fortune.com/2025/12/02/sam-altman-declares-code-red-google-gemini-ceo-sundar-pichai/](https://fortune.com/2025/12/02/sam-altman-declares-code-red-google-gemini-ceo-sundar-pichai/)
--- TOP COMMENTS ---
You seem giddy over this. It’s certainly interesting but the race is not over. Google has made massive strides, no doubt. For the sake of us as users, we need healthy competition. I’m a Google investor and wish the best for them but this isn’t over yet.
---
Sounds like healthy competition. Good for development...
Yesterday
OpenAI Needs to Increase Revenue by 560% Without Increasing Costs to Justify $500 Billion Valuation
--- TOP COMMENTS ---
Do yall realize big tech had over a decade to turn a profit.. look at Amazon, Meta, Google’s history. But when it comes to openAI they want profits today lol
---
No, they need to make that much profit in expectation.
Which is a very different proposition as it doesn't require the "without increasing costs" part...
Yesterday
Tools
Ministral 3B WebGPU
Ministral WebGPU optimizes and accelerates machine learning application performance
Yesterday
Transformers v5
The backbone of modern AI, re-engineered Discussion | Link
Yesterday
ReliAPI
Stop losing money on failed OpenAI and Anthropic API calls. Discussion | Link
Yesterday
Selects - cutback.video
Selects is an AI-powered assembly edit tool that helps creators and teams speed up video prep
Yesterday
Gleam
AI-powered design reviews from 10 expert panelists Discussion | Link
Yesterday
Textideo
Textideo is an AI-powered video generation tool that transforms text prompts and images into 1080p videos
Yesterday
NanoBananaPro.com
Nano Banana Pro is an AI image generator powered by Gemini 3 Pro
Yesterday
imgtotext.ai
Image Text Converter is an AI-based OCR tool for fast text extraction from images
Yesterday
imgeditor.co
Edit images with Nano Banana, Google's recent AI image editor that uses natural language text prompts to transform images
Yesterday
Models
GPT-5.1 vs Gemini 3 vs Claude Opus 4.5 on real data analysis tasks.
I have been intrigued by the different AI models and how they respond to different query types. Especially for real-world use cases, where problems are a bit more specific than generic.
I tried testing it with data analytics prompts and wanted to see which one actually delivers when you're not just asking it to "analyze this CSV", like real messy business problems where the answer isn't obvious.
The idea was to see which one is most efficient in understanding the prompt and giving real-world solutions.
What I tested:
1. **Sales anomaly** \- Q3 revenue dropped 4.8% then surged 21% in Q4. What happened and where should we investigate?
2. **Hidden pattern in SaaS metrics** \- Overall conversion is 18%, but users who complete tutorial AND invite a teammate convert at 67%. What's the real insight?
3. **Statistical trap** \- Site A: 10k visitors, 2% conversion. Site B: 500 visitors, 3% conversion. Boss says "B is clearly better." Is he right?
How the models responded:
**Claude Opus 4.5** was the most organized. Clear tables, triage frameworks ("check if Q2 was weird first - takes 10 minutes"), segmentation matrices. Best for presenting to non-technical people but didn't have those strategic "aha" moments.
https://preview.redd.it/fslsojwig05g1.png?width=2170&format=png&auto=webp&s=3729ba043f17cfc3e483f7b8edc79fbfd440a097
[](https://preview.redd.it/tested-gpt-5-1-gemini-3-and-claude-opus-4-5-on-real-data-v0-prajsmoz105g1.png?width=2165&format=png&auto=webp&s=92b5c75b1f798abe44ef1d6ad174a60dab411271)
**GPT-5.1** went full consultant mode every time. Detailed hypotheses, multiple scenarios, product roadmaps with specific button copy and email sequences. Super thorough but honestly felt like it was padding the response. When I needed a 2-page memo, it gave me a 10-page report. The statistical analysis was rigorous though - full z-tests, confidence intervals, sample size calculations.
**Gemini 3** consistently reframed the problem in ways that changed how I thought about it. For the sales dip, it said "break Q3 into monthly data - if it's gradual it's market fatigue, if it's a cliff something broke internally." Then dropped: "If you sell high-value contracts, one delayed deal creates this exact pattern." That's weirdly specific business intuition. For the SaaS metrics it said: **"You have an 88% single-player problem in a multiplayer product."** Only 12% of users add teammates, but that's clearly where your value is. Not a conversion problem - a positioning problem.
For the stats trap: "If 5 people on Site B clicked Back instead of Buy, your advantage disappears. You're making company decisions based on 5 people." No formulas needed - you just *feel* how fragile it is.
**My actual takeaway:**
Gemini keeps catching business context that feels almost human. The "delayed deal" insight, the "single-player problem" framing - that's not just pattern matching, it's understanding how companies actually operate.
GPT is your go-to when you need to defend conclusions with full statistical rigor. Just be ready for more content than you probably need.
Claude makes everything clear and actionable but plays it safer. Good for exec presentations.
If I'm being real, I'd probably run Gemini first for strategic insight, then validate with GPT's stats if needed, then use Claude's formatting for the final deck.
[Full breakdown with actual response screenshots](https://www.khons.co/blog/best-ai-for-data-analysis-2025-gpt-5-1-vs-gemini-3-vs-claude-opus-4-5-tested)
Anyone else running these models on actual messy datasets? Curious what you're seeing on more technical stuff like time series or cohort analysis, my tests were maybe more reasoning heavy.
--- TOP COMMENTS ---
GPT5.1 needs clear verbosity limits in the prompt. That's what their model dev site says.
---
tl;dr. I appreciate you but, it's 7:00 pm on a Wednesday. I just don't have time for all that...
I tried testing it with data analytics prompts and wanted to see which one actually delivers when you're not just asking it to "analyze this CSV", like real messy business problems where the answer isn't obvious.
The idea was to see which one is most efficient in understanding the prompt and giving real-world solutions.
What I tested:
1. **Sales anomaly** \- Q3 revenue dropped 4.8% then surged 21% in Q4. What happened and where should we investigate?
2. **Hidden pattern in SaaS metrics** \- Overall conversion is 18%, but users who complete tutorial AND invite a teammate convert at 67%. What's the real insight?
3. **Statistical trap** \- Site A: 10k visitors, 2% conversion. Site B: 500 visitors, 3% conversion. Boss says "B is clearly better." Is he right?
How the models responded:
**Claude Opus 4.5** was the most organized. Clear tables, triage frameworks ("check if Q2 was weird first - takes 10 minutes"), segmentation matrices. Best for presenting to non-technical people but didn't have those strategic "aha" moments.
https://preview.redd.it/fslsojwig05g1.png?width=2170&format=png&auto=webp&s=3729ba043f17cfc3e483f7b8edc79fbfd440a097
[](https://preview.redd.it/tested-gpt-5-1-gemini-3-and-claude-opus-4-5-on-real-data-v0-prajsmoz105g1.png?width=2165&format=png&auto=webp&s=92b5c75b1f798abe44ef1d6ad174a60dab411271)
**GPT-5.1** went full consultant mode every time. Detailed hypotheses, multiple scenarios, product roadmaps with specific button copy and email sequences. Super thorough but honestly felt like it was padding the response. When I needed a 2-page memo, it gave me a 10-page report. The statistical analysis was rigorous though - full z-tests, confidence intervals, sample size calculations.
**Gemini 3** consistently reframed the problem in ways that changed how I thought about it. For the sales dip, it said "break Q3 into monthly data - if it's gradual it's market fatigue, if it's a cliff something broke internally." Then dropped: "If you sell high-value contracts, one delayed deal creates this exact pattern." That's weirdly specific business intuition. For the SaaS metrics it said: **"You have an 88% single-player problem in a multiplayer product."** Only 12% of users add teammates, but that's clearly where your value is. Not a conversion problem - a positioning problem.
For the stats trap: "If 5 people on Site B clicked Back instead of Buy, your advantage disappears. You're making company decisions based on 5 people." No formulas needed - you just *feel* how fragile it is.
**My actual takeaway:**
Gemini keeps catching business context that feels almost human. The "delayed deal" insight, the "single-player problem" framing - that's not just pattern matching, it's understanding how companies actually operate.
GPT is your go-to when you need to defend conclusions with full statistical rigor. Just be ready for more content than you probably need.
Claude makes everything clear and actionable but plays it safer. Good for exec presentations.
If I'm being real, I'd probably run Gemini first for strategic insight, then validate with GPT's stats if needed, then use Claude's formatting for the final deck.
[Full breakdown with actual response screenshots](https://www.khons.co/blog/best-ai-for-data-analysis-2025-gpt-5-1-vs-gemini-3-vs-claude-opus-4-5-tested)
Anyone else running these models on actual messy datasets? Curious what you're seeing on more technical stuff like time series or cohort analysis, my tests were maybe more reasoning heavy.
--- TOP COMMENTS ---
GPT5.1 needs clear verbosity limits in the prompt. That's what their model dev site says.
---
tl;dr. I appreciate you but, it's 7:00 pm on a Wednesday. I just don't have time for all that...
Yesterday
Kling AI 2.6 Just Dropped: First Text to Video Model With Built-in Audio & 1080p Output
Kling AI just launched **Kling 2.6** and it’s no longer silent video AI.
• Native audio + visuals in one generation.
• 1080p video output.
• Filmmaker-focused Pro API (Artlist).
• Better character consistency across shots.
**Is this finally the beginning of real AI filmmaking?**
--- TOP COMMENTS ---
10 mins later: Qwen just dropped Qwen video 5.3 the newest best ai video model.
---
AI zoom intensifies..
• Native audio + visuals in one generation.
• 1080p video output.
• Filmmaker-focused Pro API (Artlist).
• Better character consistency across shots.
**Is this finally the beginning of real AI filmmaking?**
--- TOP COMMENTS ---
10 mins later: Qwen just dropped Qwen video 5.3 the newest best ai video model.
---
AI zoom intensifies..
Yesterday
Kling 2.6 is Now Available on fal
Tl;dr: NVIDIA's Kling 2.6 model is now available on the fal platform, enabling native audio generation for text-to-video and image-to-video tasks.
Key Takeaways:
- Kling 2.6 allows for native audio generation, enhancing creative possibilities across various use cases, including cinematic videos, visual and sound effects, product ads, and social media content.
- The model demonstrates improved realism in audio performances, detailed visuals, and precise lip-sync, enhancing the overall quality of generated videos.
- Kling 2.6 is available exclusively on the fal platform, expanding creative possibilities for developers and content creators.
Yesterday
[R] [N] TabPFN now scales to millions of rows (tabular foundation model)
Context: TabPFN is a pretrained transformer trained on more than hundred million synthetic datasets to perform in-context learning and output a predictive distribution for the test data. It natively supports missing values, categorical features, text and numerical features is robust to outliers and uninformative features. Published in Nature earlier this year, currently #1 on TabArena: [https://huggingface.co/TabArena](https://huggingface.co/TabArena)
In January, TabPFNv2 handled 10K rows, a month ago 50K & 100K rows and now there is a Scaling Mode where we're showing strong performance up to 10M.
Scaling Mode is a new pipeline around TabPFN-2.5 that removes the fixed row constraint. On our internal benchmarks (1M-10M rows), it's competitive with tuned gradient boosting and continues to improve.
Technical blog post with benchmarks: [https://priorlabs.ai/technical-reports/large-data-model](https://priorlabs.ai/technical-reports/large-data-model)
We welcome feedback and thoughts!
--- TOP COMMENTS ---
That's exciting since my job is still very much in the tabular world. Still rocking xgboost and lightgbm.
>output a predictive distribution
I might have missed this, but what methodology are you using to generate a distribution rather than a point estimate?
---
How do you know that you are not overfitting to the tab arena evals? Given how much data you train on, it seems likely?..
In January, TabPFNv2 handled 10K rows, a month ago 50K & 100K rows and now there is a Scaling Mode where we're showing strong performance up to 10M.
Scaling Mode is a new pipeline around TabPFN-2.5 that removes the fixed row constraint. On our internal benchmarks (1M-10M rows), it's competitive with tuned gradient boosting and continues to improve.
Technical blog post with benchmarks: [https://priorlabs.ai/technical-reports/large-data-model](https://priorlabs.ai/technical-reports/large-data-model)
We welcome feedback and thoughts!
--- TOP COMMENTS ---
That's exciting since my job is still very much in the tabular world. Still rocking xgboost and lightgbm.
>output a predictive distribution
I might have missed this, but what methodology are you using to generate a distribution rather than a point estimate?
---
How do you know that you are not overfitting to the tab arena evals? Given how much data you train on, it seems likely?..
Yesterday
I'm surprised how simple Qwen3 VL's architecture is.
the new 3D position id logic really got a lot more intuitive compared to qwen2.5 vl. it basically index image patches on width and height dimension in addition to the regular token sequence / temporal dimension (while treating text as one same number across all 3 dimensions).
in addition to this, they added deepstack, which essentially is just some residual connections between vision encoder blocks and downstream LLM blocks.
here's the full repo if you want to read more: [https://github.com/Emericen/tiny-qwen](https://github.com/Emericen/tiny-qwen)
--- TOP COMMENTS ---
Most machine learning architecture isn’t really that complicated when you look at it in code. Plus, in software development simplicity = better.
---
Yeah there are much more complex archs than llms..
in addition to this, they added deepstack, which essentially is just some residual connections between vision encoder blocks and downstream LLM blocks.
here's the full repo if you want to read more: [https://github.com/Emericen/tiny-qwen](https://github.com/Emericen/tiny-qwen)
--- TOP COMMENTS ---
Most machine learning architecture isn’t really that complicated when you look at it in code. Plus, in software development simplicity = better.
---
Yeah there are much more complex archs than llms..
Yesterday
DeepSeek V3.2 Technical Report
Here is a brief summary of **key breakthroughs of DeepSeek V3.2**
**1. DeepSeek Sparse Attention (DSA)**
A new efficient attention mechanism that dramatically reduces computational complexity while preserving performance in long-context scenarios.
It uses a lightning indexer with fine-grained top-k token selection to achieve sparse but effective attention.
**2. Scalable and Stable Reinforcement Learning Framework**
Implements a heavily scaled post-training RL pipeline, with compute exceeding 10% of pretraining cost.
**3. Large-Scale Agentic Task Synthesis Pipeline**
Provides a novel pipeline that programmatically generates large numbers of tool-use environments (1,800+ environments, 85,000+ complex prompts).
This boosts generalization, tool-use ability, and instruction-following in interactive settings.
**4. Unified Reasoning + Agentic RL Training**
Merges reasoning, tool-use, and human-alignment RL into a single stage rather than multi-stage pipelines.
This avoids catastrophic forgetting and improves cross-domain performance simultaneously.
**DeepSeek-V3.2-Speciale**
A high-compute variant trained with relaxed length penalties and enhanced mathematical-reasoning rewards.
This model even surpasses GPT-5 and exhibits reasoning proficiency on par with Gemini-3.0-Pro, achieving gold-medal performance in both the 2025 International Mathematical Olympiad (IMO) and the International Olympiad in Informatics (IOI).
[Arxiv paper ](https://arxiv.org/abs/2512.02556)
--- TOP COMMENTS ---
Not sure if it's a special offer or if it's truly cheaper to run.
The openrouter (now it's fashionable to put open in all closed things?) has dirt cheap provider prices.
---
ALL providers from openrouter with the exception of deepseek offer quantized models at the same price as DeepSeek , minus the quality. I think at this point OpenRouter is a psy-op anti open llms...
**1. DeepSeek Sparse Attention (DSA)**
A new efficient attention mechanism that dramatically reduces computational complexity while preserving performance in long-context scenarios.
It uses a lightning indexer with fine-grained top-k token selection to achieve sparse but effective attention.
**2. Scalable and Stable Reinforcement Learning Framework**
Implements a heavily scaled post-training RL pipeline, with compute exceeding 10% of pretraining cost.
**3. Large-Scale Agentic Task Synthesis Pipeline**
Provides a novel pipeline that programmatically generates large numbers of tool-use environments (1,800+ environments, 85,000+ complex prompts).
This boosts generalization, tool-use ability, and instruction-following in interactive settings.
**4. Unified Reasoning + Agentic RL Training**
Merges reasoning, tool-use, and human-alignment RL into a single stage rather than multi-stage pipelines.
This avoids catastrophic forgetting and improves cross-domain performance simultaneously.
**DeepSeek-V3.2-Speciale**
A high-compute variant trained with relaxed length penalties and enhanced mathematical-reasoning rewards.
This model even surpasses GPT-5 and exhibits reasoning proficiency on par with Gemini-3.0-Pro, achieving gold-medal performance in both the 2025 International Mathematical Olympiad (IMO) and the International Olympiad in Informatics (IOI).
[Arxiv paper ](https://arxiv.org/abs/2512.02556)
--- TOP COMMENTS ---
Not sure if it's a special offer or if it's truly cheaper to run.
The openrouter (now it's fashionable to put open in all closed things?) has dirt cheap provider prices.
---
ALL providers from openrouter with the exception of deepseek offer quantized models at the same price as DeepSeek , minus the quality. I think at this point OpenRouter is a psy-op anti open llms...
Yesterday
Products
Kling AI 2.6 just launched — first version with native audio and 1080p video
Kling AI just launched **Kling 2.6** and it’s no longer silent video AI.
• Native audio + visuals in one generation.
• 1080p video output.
• Filmmaker-focused Pro API (Artlist).
• Better character consistency across shots.
**Is this finally the beginning of real AI filmmaking?**
--- TOP COMMENTS ---
I'll stick with WAN.
---
https://preview.redd.it/e6c8hxz7r05g1.jpeg?width=1080&format=pjpg&auto=webp&s=eaebe42221b33e2f9bb32d6935a3a5cd2b9f55de..
• Native audio + visuals in one generation.
• 1080p video output.
• Filmmaker-focused Pro API (Artlist).
• Better character consistency across shots.
**Is this finally the beginning of real AI filmmaking?**
--- TOP COMMENTS ---
I'll stick with WAN.
---
https://preview.redd.it/e6c8hxz7r05g1.jpeg?width=1080&format=pjpg&auto=webp&s=eaebe42221b33e2f9bb32d6935a3a5cd2b9f55de..
Yesterday
Workspace Studio aims to solve the real agent problem: Getting employees to use them
Tl;dr: Google Workspace Studio is now generally available, allowing employees to design and manage AI agents directly within the platform, aiming to increase agent adoption and ease of use.
Key Takeaways:
- Google has made Google Workspace Studio generally available to enable employees to access and use AI agents, directly integrating with applications such as Google Docs and Sheets.
- The platform is powered by Gemini 3 and allows developers to offload lower-priority agent tasks, while users can choose from templates or write custom prompts for agent creation.
- Workspace Studio targets business teams and offers deep integration with Workspace apps, enabling agents to understand the full context of work and provide personalized help.
- This move directly competes with Microsoft's Copilot and undercuts some integrations that brought OpenAI's ChatGPT into enterprise applications.
Yesterday
12 Months of Perplexity Pro for just $12.90 – Access GPT-5.1, Grok 4.1, Gemini 3 Pro, Claude 4.5 Sonnet, Kimi K2 Thinking, & Sonar All in One Place 🔥
Hey everyone! 👋 If you're tired of hopping between different AI subscriptions, this might be the perfect fix for your workflow.
Perplexity Pro has become my central hub for everything, it combines all the heavy-hitting AI models into a single, citation-backed interface that browses the web in real-time.
I have a limited batch of **official, global 1-year Perplexity Pro keys** available for a one-time payment of **$12.90** (saving you significantly compared to the regular **$200/year**). All transactions are secure and backed by buyer protection.
**What you unlock with Perplexity Pro:**
🤖 The Ultimate AI Dashboard: No more juggling tabs. Route your queries instantly through GPT-5.1, Gemini 3 Pro, Grok 4.1, Kimi K2 Thinking, Claude Sonnet 4.5, or Perplexity’s own Sonar. You can compare results side-by-side or use the "Best" mode to let the system pick the smartest model for your specific question. You get 300+ Pro queries daily and unlimited file uploads.
🌐 Fact-Based Answers: Unlike standard chatbots that can hallucinate, Perplexity provides answers grounded in real-time web sources with citations, making it ideal for fact-checking and deep dives.
📂 Power User Features: Perfect for complex tasks like coding, research, and long-form writing. It supports threaded discussions, unlimited file analysis (PDFs, code, docs), and distraction-free "Focus" modes.
📚 Massive Context Window: It handles large documents and multi-step instructions much better than basic free chatbots, keeping track of long conversations without losing the plot.
☄️ Comet Browser Assistant: You also get full access to Comet, an agentic browser that can execute complex, multi-step actions on the web using natural language.
🧠 Smart Memory: Perplexity remembers your project details, preferences, and past context, making it feel like a true personal assistant that knows your style across every conversation.
**Deal & Activation Info:**
🗓️ 1-Year Guaranteed Access: You get a full 12 months of Pro status for a flat **$12.90** instead of **$200.** For existing and new accounts that never had Pro before.
🔒 Safe & Secure: Payments are processed through a trusted provider with full Buyer Protection.
⚡ Fast Activation: Your key is delivered and active within moments.
🌍 Works Globally: Activate and use it from anywhere and anytime in the world.
💳 No Credit Card Required: You don’t need to link a card to redeem the code, and there are no auto-renewals to worry about.
🎖️ Tried & Tested by many: I’ve helped many users here on Reddit get set up, and the feedback has been stellar. You can check the verified vouches [Here](https://www.reddit.com/r/DigitalDealsHub/comments/1p9aebn/vouch_thread_reviews_feedback/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button) and [Here](https://www.reddit.com/user/Big_Firefighter2110/comments/1ou4oq4/perplexity_pro_vouches_info/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button).
And feel free to ask for more proofs if you wish.
Please note, I have a limited quantity left.
**If you want to grab one, DM me or drop a comment below before they're gone! 💬🔥**
**----------------------------------------------------**
***Canva Pro*** *invites are also available, ask me about it in the DM in case you want it too!*
--- TOP COMMENTS ---
I'd like one plz
---
Interested in both, thanks!..
Perplexity Pro has become my central hub for everything, it combines all the heavy-hitting AI models into a single, citation-backed interface that browses the web in real-time.
I have a limited batch of **official, global 1-year Perplexity Pro keys** available for a one-time payment of **$12.90** (saving you significantly compared to the regular **$200/year**). All transactions are secure and backed by buyer protection.
**What you unlock with Perplexity Pro:**
🤖 The Ultimate AI Dashboard: No more juggling tabs. Route your queries instantly through GPT-5.1, Gemini 3 Pro, Grok 4.1, Kimi K2 Thinking, Claude Sonnet 4.5, or Perplexity’s own Sonar. You can compare results side-by-side or use the "Best" mode to let the system pick the smartest model for your specific question. You get 300+ Pro queries daily and unlimited file uploads.
🌐 Fact-Based Answers: Unlike standard chatbots that can hallucinate, Perplexity provides answers grounded in real-time web sources with citations, making it ideal for fact-checking and deep dives.
📂 Power User Features: Perfect for complex tasks like coding, research, and long-form writing. It supports threaded discussions, unlimited file analysis (PDFs, code, docs), and distraction-free "Focus" modes.
📚 Massive Context Window: It handles large documents and multi-step instructions much better than basic free chatbots, keeping track of long conversations without losing the plot.
☄️ Comet Browser Assistant: You also get full access to Comet, an agentic browser that can execute complex, multi-step actions on the web using natural language.
🧠 Smart Memory: Perplexity remembers your project details, preferences, and past context, making it feel like a true personal assistant that knows your style across every conversation.
**Deal & Activation Info:**
🗓️ 1-Year Guaranteed Access: You get a full 12 months of Pro status for a flat **$12.90** instead of **$200.** For existing and new accounts that never had Pro before.
🔒 Safe & Secure: Payments are processed through a trusted provider with full Buyer Protection.
⚡ Fast Activation: Your key is delivered and active within moments.
🌍 Works Globally: Activate and use it from anywhere and anytime in the world.
💳 No Credit Card Required: You don’t need to link a card to redeem the code, and there are no auto-renewals to worry about.
🎖️ Tried & Tested by many: I’ve helped many users here on Reddit get set up, and the feedback has been stellar. You can check the verified vouches [Here](https://www.reddit.com/r/DigitalDealsHub/comments/1p9aebn/vouch_thread_reviews_feedback/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button) and [Here](https://www.reddit.com/user/Big_Firefighter2110/comments/1ou4oq4/perplexity_pro_vouches_info/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button).
And feel free to ask for more proofs if you wish.
Please note, I have a limited quantity left.
**If you want to grab one, DM me or drop a comment below before they're gone! 💬🔥**
**----------------------------------------------------**
***Canva Pro*** *invites are also available, ask me about it in the DM in case you want it too!*
--- TOP COMMENTS ---
I'd like one plz
---
Interested in both, thanks!..
Yesterday
New Voice Mode feature in Chat GPT
In this new ChatGPT Voice Mode, we can use the ChatGPT voice feature while typing.
We won't have to speak; we will write, and ChatGPT will respond by speaking and writing simultaneously.
This is a much-needed feature for me, as most of the time I can't use voice mode because I'm in an environment where I can't speak.
Also we can disable this mode by turning on Seperate Mode in voice setting.
--- TOP COMMENTS ---
I haven't tested it yet, but
I think I like the new voice mode. I don't like talking that much myself, but I like it when the AI answers so I don't have to read the text myself.
---
I saw that today when I accidentally clicked on it. Didn't know what it was lol..
We won't have to speak; we will write, and ChatGPT will respond by speaking and writing simultaneously.
This is a much-needed feature for me, as most of the time I can't use voice mode because I'm in an environment where I can't speak.
Also we can disable this mode by turning on Seperate Mode in voice setting.
--- TOP COMMENTS ---
I haven't tested it yet, but
I think I like the new voice mode. I don't like talking that much myself, but I like it when the AI answers so I don't have to read the text myself.
---
I saw that today when I accidentally clicked on it. Didn't know what it was lol..
Yesterday
Amazon has joined the chat
--- TOP COMMENTS ---
I’m still waiting for my package
---
Amazon is a massive investor in Anthropic, so they were well-represented in the leaderboard even before this...
Yesterday
Opus 4.5 Finally available in Claude's $20 Plan
--- TOP COMMENTS ---
You better use your prompt wisely.
---
It's already been available in the $20 Pro plan on the Claude website since it was released, maybe it just hasn't been available for Claude Code...
Yesterday
Developer Tools
AWS doubles down on custom LLMs with features meant to simplify model creation
Tl;dr: AWS announced new capabilities in Amazon Bedrock and SageMaker AI to make building and fine-tuning custom large language models easier for developers.
Key Takeaways:
- AWS introduced serverless model customization in SageMaker for customizing Amazon's own Nova models and open-source models.
- Reinforcement Fine-Tuning in Bedrock allows developers to choose a reward function or workflow for automatic model customization.
- These new capabilities are designed to help enterprise customers create unique solutions optimized for their brand and use case.
Yesterday
aws.amazon.com
Amazon Bedrock adds reinforcement fine-tuning simplifying how developers build smarter, more accurate AI models
Tl;dr: Amazon Bedrock introduces reinforcement fine-tuning, making advanced model customization accessible to developers without requiring deep ML expertise. This feature improves model accuracy by 66% on average over base models and reduces costs.
Key Takeaways:
- Amazon Bedrock now offers reinforcement fine-tuning, an advanced technique that trains models using feedback instead of massive labeled datasets, eliminating the need for ML expertise.
- The feature improves model accuracy by 66% on average over base models and reduces costs by allowing developers to use smaller, faster, and more efficient model variants.
- Reinforcement fine-tuning in Amazon Bedrock automates the workflow, making it easier for developers to build smarter, more cost-effective AI models.
- Amazon Bedrock supports two approaches to reinforcement fine-tuning: Reinforcement Learning with Verifiable Rewards (RLVR) and Reinforcement Learning from AI Feedback (RLAIF).
Yesterday
Cheapest and best way to host a GGUF model with an API (like OpenAI) for production?
Hey folks,
I'm trying to host a `.gguf` LLM in a way that lets me access it using an API — similar to how we call the OpenAI API (`/v1/chat/completions`, etc).
I want to expose my own hosted GGUF model through a clean HTTP API that any app can use.
### What I need:
1. **Host a GGUF model** (7B / 13B / possibly 30B later)
2. **Access it over a REST API** (Ollama-style, OpenAI-style, or custom)
3. **Production-ready setup** (stable, scalable enough, not hobby-only)
4. **Cheapest possible hosting options** (VPS or GPU cloud)
5. Advice on **which server/runtime is best**:
- Ollama API server
- llama.cpp server mode
- LocalAI
- vLLM (if GGUF isn’t ideal for it)
- or anything else that works well
### Budget Focus
Trying to find the **best price-to-performance platform**.
Options I'm considering but unsure about:
- Hetzner
- RunPod
- Vast.ai
- Vultr
- Lambda Labs
- Any cheap GPU rental providers?
### My goals:
- Host the model once
- Call it from my mobile or backend app through an API
- Avoid OpenAI-style monthly costs
- Keep latency reasonable
- Ensure it runs reliably even with multiple requests
### Questions:
- What’s the **cheapest** but still **practical** setup for production?
- Is **Ollama on a VPS** good enough?
- Should I use **llama.cpp server** instead?
- Does anyone run GGUF models in production at scale?
- Any recommended architectures or pitfalls?
Would really appreciate hearing what setups have worked for you — especially from people who have deployed GGUF models behind an API for real apps!
Thanks in advance
--- TOP COMMENTS ---
It's been proven time and time again - self-hostable LLMs are much more expensive compared to API. Only makes sense for air gapped deployment or when data is highly sensitive
---
In my research VLLM is the only thing that is scalable, but want to know if any others disagree..
I'm trying to host a `.gguf` LLM in a way that lets me access it using an API — similar to how we call the OpenAI API (`/v1/chat/completions`, etc).
I want to expose my own hosted GGUF model through a clean HTTP API that any app can use.
### What I need:
1. **Host a GGUF model** (7B / 13B / possibly 30B later)
2. **Access it over a REST API** (Ollama-style, OpenAI-style, or custom)
3. **Production-ready setup** (stable, scalable enough, not hobby-only)
4. **Cheapest possible hosting options** (VPS or GPU cloud)
5. Advice on **which server/runtime is best**:
- Ollama API server
- llama.cpp server mode
- LocalAI
- vLLM (if GGUF isn’t ideal for it)
- or anything else that works well
### Budget Focus
Trying to find the **best price-to-performance platform**.
Options I'm considering but unsure about:
- Hetzner
- RunPod
- Vast.ai
- Vultr
- Lambda Labs
- Any cheap GPU rental providers?
### My goals:
- Host the model once
- Call it from my mobile or backend app through an API
- Avoid OpenAI-style monthly costs
- Keep latency reasonable
- Ensure it runs reliably even with multiple requests
### Questions:
- What’s the **cheapest** but still **practical** setup for production?
- Is **Ollama on a VPS** good enough?
- Should I use **llama.cpp server** instead?
- Does anyone run GGUF models in production at scale?
- Any recommended architectures or pitfalls?
Would really appreciate hearing what setups have worked for you — especially from people who have deployed GGUF models behind an API for real apps!
Thanks in advance
--- TOP COMMENTS ---
It's been proven time and time again - self-hostable LLMs are much more expensive compared to API. Only makes sense for air gapped deployment or when data is highly sensitive
---
In my research VLLM is the only thing that is scalable, but want to know if any others disagree..
Yesterday
Turn Your Slack Conversations into GitHub PRs
Tl;dr: Continue launched a new integration that enables AI agents to create GitHub issues, generate PRs, and automate workflows directly from Slack conversations.
Key Takeaways:
- Continue's Slack integration grants AI agents full context of a team's conversations, allowing them to create issues, PRs, and automate workflows.
- The integration enables seamless handoffs between Slack conversations and GitHub work, reducing friction and increasing productivity.
- Developers can use the integration to automate tedious tasks, such as creating issues, assigning tasks, and generating PRs.
Yesterday
I built an open-source tool to stop Claude Code from re-reading my files every session (Persistent Memory)
I got tired of the 'Context Tax.'
Every time I started a new session, I was watching Claude re-explore my codebase, read files it read yesterday, and burn tokens just to get back to where we left off.
**So I built** [Grov](https://grov.dev)**.** It’s a local CLI tool that injects past reasoning into new sessions. It basically gives Claude 'long-term memory' so it skips the exploration phase and starts coding immediately.
**The Roadmap:** Right now it runs locally, but I'm building a 'Team Sync' feature next so my instance knows what my co-founder changed while I was away. There is also an anti drift feature implemented, but it is still early (more detailed on github).
**Status**:
\- Fully Open Source (Apache 2.0)
\- Live on NPM (`npm install -g grov`)
\- 400+ downloads in the last couple of days
I’d love for this community to roast the code or give feedback. Contributions are welcome.
(note: code is early; expect some bugs)
Repo: [Grov Github Repo](https://github.com/TonyStef/Grov)
--- TOP COMMENTS ---
This sounds interesting. I haven't had time to check for myself, but I'm curious, when you say inject past reasoning into new sessions do you literally mean you're editing the JSONL files?
---
How can possibly know what past memories to inject?
Is it relevant to current request?
Does it contradict the current request?
How can you possibly determine what past memories are valuable to the current request?
I wouldnt use this personally - - I prefer tight control on the context of the prompts sent to LLMs..
Every time I started a new session, I was watching Claude re-explore my codebase, read files it read yesterday, and burn tokens just to get back to where we left off.
**So I built** [Grov](https://grov.dev)**.** It’s a local CLI tool that injects past reasoning into new sessions. It basically gives Claude 'long-term memory' so it skips the exploration phase and starts coding immediately.
**The Roadmap:** Right now it runs locally, but I'm building a 'Team Sync' feature next so my instance knows what my co-founder changed while I was away. There is also an anti drift feature implemented, but it is still early (more detailed on github).
**Status**:
\- Fully Open Source (Apache 2.0)
\- Live on NPM (`npm install -g grov`)
\- 400+ downloads in the last couple of days
I’d love for this community to roast the code or give feedback. Contributions are welcome.
(note: code is early; expect some bugs)
Repo: [Grov Github Repo](https://github.com/TonyStef/Grov)
--- TOP COMMENTS ---
This sounds interesting. I haven't had time to check for myself, but I'm curious, when you say inject past reasoning into new sessions do you literally mean you're editing the JSONL files?
---
How can possibly know what past memories to inject?
Is it relevant to current request?
Does it contradict the current request?
How can you possibly determine what past memories are valuable to the current request?
I wouldnt use this personally - - I prefer tight control on the context of the prompts sent to LLMs..
Yesterday
Tracing Hanging and Complicated GPU Kernels Down To The Source Code
Tl;dr: Tracing Hanging and Complicated GPU Kernels Down To The Source Code: Developers can use user-induced GPU core dump generation, coupled with line information provided by compiling kernels with -lineinfo flag, to effectively identify and debug problematic GPU kernels.
Key Takeaways:
- Developers can now identify and debug problematic GPU kernels using a combination of user-induced GPU core dump generation and line information.
- User-induced GPU core dump generation allows developers to pinpoint the exact line of source code that triggered an issue.
- Compiling kernels with the -lineinfo flag can provide granular information needed to solve complex debugging issues.
Yesterday
Vibe Commander: Vibe code like it’s 1986
Have been building this over the last few days as a tool to bring my flow from 99% there to 100%.
I nowadays do pretty much everything using Claude Code and only ever hop into other terminal tabs to view the occasional file or run some git commands.
Vision was to have these minimal facilities in a familiar IDE style layout (Integrated Vibe Environment?) that evokes old time Norton Commander memories.
Check it out here: https://github.com/AvitalTamir/vibecommander
--- TOP COMMENTS ---
This looks very cool will definitely try it out
---
Commodore vibes...
I nowadays do pretty much everything using Claude Code and only ever hop into other terminal tabs to view the occasional file or run some git commands.
Vision was to have these minimal facilities in a familiar IDE style layout (Integrated Vibe Environment?) that evokes old time Norton Commander memories.
Check it out here: https://github.com/AvitalTamir/vibecommander
--- TOP COMMENTS ---
This looks very cool will definitely try it out
---
Commodore vibes...
Yesterday
Infrastructure
aws.amazon.com
New serverless customization in Amazon SageMaker AI accelerates model fine-tuning
Tl;dr: Amazon SageMaker AI now enables serverless model fine-tuning with popular AI models, reducing the process from months to days through easy-to-use interface and auto-provisioning of compute resources.
Key Takeaways:
- Amazon SageMaker AI supports serverless model fine-tuning with AI models like Nova, DeepSeek, GPT-OSS, Llama, and Qwen, accelerating the process from months to days.
- The service provides an easy-to-use interface for fine-tuning techniques like reinforcement learning and allows seamless model evaluation and deployment without managing infrastructure.
- Serverless MLflow capability enables logging of critical experiment metrics without modifying code and access to rich visualizations for further analysis.
- Amazon SageMaker AI is available in US East, US West, Asia Pacific, and Europe Regions with a pay-as-you-go pricing model based on the tokens processed during training and inference.
Yesterday
aws.amazon.com
Introducing checkpointless and elastic training on Amazon SageMaker HyperPod
Tl;dr: Amazon SageMaker HyperPod now offers checkpointless training that eliminates the need for traditional checkpoint-based recovery, and elastic training that enables AI workloads to automatically scale based on resource availability.
Key Takeaways:
- Amazon SageMaker HyperPod introduces checkpointless training, reducing recovery time from hours to minutes and increasing forward training momentum.
- Elastic training enables AI workloads to automatically scale based on resource availability, maximizing cluster utilization and reducing engineering time spent on reconfiguring training jobs.
- These new training techniques are designed to eliminate traditional checkpoint dependencies and fully utilize available capacity, reducing model training completion times.
- Amazon SageMaker HyperPod is offering both features in all regions, with no additional cost, and invites users to try them out and send feedback.
Yesterday
Amazon challenges competitors with on-premises Nvidia ‘AI Factories’
Tl;dr: AWS announced AI Factories, allowing businesses to run AI systems on-premises, addressing data sovereignty concerns and competing directly with Nvidia's offerings.
Key Takeaways:
- AWS, in collaboration with Nvidia, introduced AI Factories, enabling companies to deploy and manage AI systems within their own data centers.
- Customers can use Nvidia's Blackwell GPUs or Amazon's Trainium3 chip, leveraging AWS' networking, storage, and security solutions for secure data processing.
- This move targets businesses seeking to maintain data control and compete with cloud-based AI services offered by major providers like AWS itself and Microsoft.
- AI Factories may mark a trend in corporate investment in private data centers and hybrid clouds, driven by the need for data sovereignty and control.
Yesterday
Open Source
apple/CLaRa-7B-Instruct · Hugging Face
[https://huggingface.co/apple/CLaRa-7B-Instruct](https://huggingface.co/apple/CLaRa-7B-Instruct)
[https://huggingface.co/apple/CLaRa-7B-Base](https://huggingface.co/apple/CLaRa-7B-Base)
[https://huggingface.co/apple/CLaRa-7B-E2E](https://huggingface.co/apple/CLaRa-7B-E2E)
--- TOP COMMENTS ---
https://preview.redd.it/tb73dbwdkz4g1.png?width=598&format=png&auto=webp&s=c01955ea77803c14f9e0c5b9a31eda32a75cd7de
---
So it’s kind of what Siri should have been by now?
“The CLaRa-7B-Instruct model is our instruction-tuned unified RAG model with built-in semantic document compression (16× & 128x).
It supports instruction-following QA directly from compressed document representations.
Training recipe: Instruction tuning on QA-style tasks built on top of the base semantic compression model.
Benchmarks: Strong instruction-following performance under 16× compression.”..
[https://huggingface.co/apple/CLaRa-7B-Base](https://huggingface.co/apple/CLaRa-7B-Base)
[https://huggingface.co/apple/CLaRa-7B-E2E](https://huggingface.co/apple/CLaRa-7B-E2E)
--- TOP COMMENTS ---
https://preview.redd.it/tb73dbwdkz4g1.png?width=598&format=png&auto=webp&s=c01955ea77803c14f9e0c5b9a31eda32a75cd7de
---
So it’s kind of what Siri should have been by now?
“The CLaRa-7B-Instruct model is our instruction-tuned unified RAG model with built-in semantic document compression (16× & 128x).
It supports instruction-following QA directly from compressed document representations.
Training recipe: Instruction tuning on QA-style tasks built on top of the base semantic compression model.
Benchmarks: Strong instruction-following performance under 16× compression.”..
Yesterday
Is DeepSeek's new model the latest blow to proprietary AI?
Tl;dr: DeepSeek's V3.2 model outperforms some proprietary models, offers cost savings, and narrows the gap between open and proprietary models.
Key Takeaways:
- DeepSeek released its V3.2 model, aiming to help the open-source AI community catch up with proprietary models.
- The new model has two versions: 'Thinking' and 'Speciale', with the latter outperforming some leading proprietary models on certain benchmarks.
- DeepSeek's V3.2 achieves gold-level performance in the International Math Olympiad and the International Olympiad in Informatics.
- The company introduced DeepSeek Sparse Attention (DSA) to mitigate computation complexity without sacrificing long-context performance.
Yesterday
I built a Mistral inference engine from scratch
I spent the last 7 months working on my most hardcore project yet: [Torchless](https://github.com/ryanssenn/torchless). It's a pure C/C++ inference engine built entirely from scratch to run LLMs locally. I built this project to understand how LLMs actually work under the hood without relying on existing frameworks.
As of now, I have implemented the following:
\- Model Loader: Loads the billions of weights into memory necessary to run the model.
\- Tokenizer: Transforms the user input into tokens the model understands (custom BPE).
\- Tensor Backend: Supports math operations like matrix multiplications.
\- Architecture: I implemented Mistral 7B, which is one of the smaller open-source, yet very strong models.
I now have a working prototype of the engine that you can run locally. I aim to keep the code lightweight so people can learn how a large language model like ChatGPT actually generates tokens. It's all just math! Mostly matmuls ;)
The goal of the project is now to achieve maximum speed on CPU/GPU and support more advanced architectures. I am open to receiving feedback about the code, especially for performance improvements or receiving any ideas on how I should guide the project going forward!
[https://github.com/ryanssenn/torchless](https://github.com/ryanssenn/torchless)
[https://x.com/ryanssenn](https://x.com/ryanssenn)
--- TOP COMMENTS ---
This is really cool.
---
Can you pls share with us what is the cost?
Tools you use?
And did you did it all by yourself?..
As of now, I have implemented the following:
\- Model Loader: Loads the billions of weights into memory necessary to run the model.
\- Tokenizer: Transforms the user input into tokens the model understands (custom BPE).
\- Tensor Backend: Supports math operations like matrix multiplications.
\- Architecture: I implemented Mistral 7B, which is one of the smaller open-source, yet very strong models.
I now have a working prototype of the engine that you can run locally. I aim to keep the code lightweight so people can learn how a large language model like ChatGPT actually generates tokens. It's all just math! Mostly matmuls ;)
The goal of the project is now to achieve maximum speed on CPU/GPU and support more advanced architectures. I am open to receiving feedback about the code, especially for performance improvements or receiving any ideas on how I should guide the project going forward!
[https://github.com/ryanssenn/torchless](https://github.com/ryanssenn/torchless)
[https://x.com/ryanssenn](https://x.com/ryanssenn)
--- TOP COMMENTS ---
This is really cool.
---
Can you pls share with us what is the cost?
Tools you use?
And did you did it all by yourself?..
Yesterday
Research
Nvidia's new AI framework trains an 8B model to manage tools like a pro
Tl;dr: Nvidia and the University of Hong Kong release Orchestrator, an 8-billion-parameter model that coordinates tools and LLMs for complex problem-solving. This model outperforms larger models at a lower cost and improves efficiency.
Key Takeaways:
- Researchers at Nvidia and the University of Hong Kong developed Orchestrator, an 8-billion-parameter model that coordinates tool use for complex problems.
- The model uses a new reinforcement learning framework called ToolOrchestra to train a small 'orchestrator' model to manage specialized models and tools.
- Orchestrator outperforms larger models on three challenging benchmarks at a lower cost and improved efficiency, making it a promising approach for building scalable AI reasoning systems.
Yesterday
Tested GPT-5.1, Gemini 3, and Claude Opus 4.5 on actual data analysis problems. All models performed different.
Been working with AI models for data work lately and wanted to see which one actually delivers when you're not just asking it to "analyze this CSV", like real messy business problems where the answer isn't obvious.
The idea was to see which one is most efficient in understanding the prompt and giving real world solutions.
What I tested:
1. **Sales anomaly** \- Q3 revenue dropped 4.8% then surged 21% in Q4. What happened and where should we investigate?
2. **Hidden pattern in SaaS metrics** \- Overall conversion is 18%, but users who complete tutorial AND invite a teammate convert at 67%. What's the real insight?
3. **Statistical trap** \- Site A: 10k visitors, 2% conversion. Site B: 500 visitors, 3% conversion. Boss says "B is clearly better." Is he right?
How the models responded:
**Claude Opus 4.5** was the most organized. Clear tables, triage frameworks ("check if Q2 was weird first - takes 10 minutes"), segmentation matrices. Best for presenting to non-technical people but didn't have those strategic "aha" moments.
[](https://preview.redd.it/tested-gpt-5-1-gemini-3-and-claude-opus-4-5-on-real-data-v0-prajsmoz105g1.png?width=2165&format=png&auto=webp&s=92b5c75b1f798abe44ef1d6ad174a60dab411271)
**GPT-5.1** went full consultant mode every time. Detailed hypotheses, multiple scenarios, product roadmaps with specific button copy and email sequences. Super thorough but honestly felt like it was padding the response. When I needed a 2-page memo, it gave me a 10-page report. The statistical analysis was rigorous though - full z-tests, confidence intervals, sample size calculations.
**Gemini 3** consistently reframed the problem in ways that changed how I thought about it. For the sales dip, it said "break Q3 into monthly data - if it's gradual it's market fatigue, if it's a cliff something broke internally." Then dropped: "If you sell high-value contracts, one delayed deal creates this exact pattern." That's weirdly specific business intuition. For the SaaS metrics it said: **"You have an 88% single-player problem in a multiplayer product."** Only 12% of users add teammates, but that's clearly where your value is. Not a conversion problem - a positioning problem.
For the stats trap: "If 5 people on Site B clicked Back instead of Buy, your advantage disappears. You're making company decisions based on 5 people." No formulas needed - you just *feel* how fragile it is.
https://preview.redd.it/cizjmsilh05g1.png?width=2164&format=png&auto=webp&s=e69fc5ac4832ce834b6705e5d344f2915d7ea27e
**My actual takeaway:**
Gemini keeps catching business context that feels almost human. The "delayed deal" insight, the "single-player problem" framing - that's not just pattern matching, it's understanding how companies actually operate.
GPT is your go-to when you need to defend conclusions with full statistical rigor. Just be ready for more content than you probably need.
Claude makes everything clear and actionable but plays it safer. Good for exec presentations.
If I'm being real, I'd probably run Gemini first for strategic insight, then validate with GPT's stats if needed, then use Claude's formatting for the final deck.
[Full breakdown with actual response screenshots](https://www.khons.co/blog/best-ai-for-data-analysis-2025-gpt-5-1-vs-gemini-3-vs-claude-opus-4-5-tested)
Anyone else running these models on actual messy datasets? Curious what you're seeing on more technical stuff like time series or cohort analysis, my tests were maybe more reasoning heavy.
--- TOP COMMENTS ---
Cleverly designed marketing post for your blog. The odd casual wording was made to look organic, and the engagement bait at the end was meant to drive the algorithm for views, while looking like you genuinely wanted others experiences. (You don't, you want views)
No one trusts this post. It's not real.
Thumbs DOWN
---
Just a general note for anyone reading (not a criticism of this OP in particular): posts like this are usually closer to anecdotal product impressions than to an actual experiment or study. The prompts are often hand-picked, the number of trials is small, and there is no real control or statistical analysis, so the results should not be treated as evidence that "model X is better than model Y." They can still be useful for seeing example prompts and how different models "feel" to use, but it is best to treat the linked blog as marketing plus personal experience, not as rigorous evaluation. That is usually best-case; worst-case it is just advertising uBlock can't detect...
The idea was to see which one is most efficient in understanding the prompt and giving real world solutions.
What I tested:
1. **Sales anomaly** \- Q3 revenue dropped 4.8% then surged 21% in Q4. What happened and where should we investigate?
2. **Hidden pattern in SaaS metrics** \- Overall conversion is 18%, but users who complete tutorial AND invite a teammate convert at 67%. What's the real insight?
3. **Statistical trap** \- Site A: 10k visitors, 2% conversion. Site B: 500 visitors, 3% conversion. Boss says "B is clearly better." Is he right?
How the models responded:
**Claude Opus 4.5** was the most organized. Clear tables, triage frameworks ("check if Q2 was weird first - takes 10 minutes"), segmentation matrices. Best for presenting to non-technical people but didn't have those strategic "aha" moments.
[](https://preview.redd.it/tested-gpt-5-1-gemini-3-and-claude-opus-4-5-on-real-data-v0-prajsmoz105g1.png?width=2165&format=png&auto=webp&s=92b5c75b1f798abe44ef1d6ad174a60dab411271)
**GPT-5.1** went full consultant mode every time. Detailed hypotheses, multiple scenarios, product roadmaps with specific button copy and email sequences. Super thorough but honestly felt like it was padding the response. When I needed a 2-page memo, it gave me a 10-page report. The statistical analysis was rigorous though - full z-tests, confidence intervals, sample size calculations.
**Gemini 3** consistently reframed the problem in ways that changed how I thought about it. For the sales dip, it said "break Q3 into monthly data - if it's gradual it's market fatigue, if it's a cliff something broke internally." Then dropped: "If you sell high-value contracts, one delayed deal creates this exact pattern." That's weirdly specific business intuition. For the SaaS metrics it said: **"You have an 88% single-player problem in a multiplayer product."** Only 12% of users add teammates, but that's clearly where your value is. Not a conversion problem - a positioning problem.
For the stats trap: "If 5 people on Site B clicked Back instead of Buy, your advantage disappears. You're making company decisions based on 5 people." No formulas needed - you just *feel* how fragile it is.
https://preview.redd.it/cizjmsilh05g1.png?width=2164&format=png&auto=webp&s=e69fc5ac4832ce834b6705e5d344f2915d7ea27e
**My actual takeaway:**
Gemini keeps catching business context that feels almost human. The "delayed deal" insight, the "single-player problem" framing - that's not just pattern matching, it's understanding how companies actually operate.
GPT is your go-to when you need to defend conclusions with full statistical rigor. Just be ready for more content than you probably need.
Claude makes everything clear and actionable but plays it safer. Good for exec presentations.
If I'm being real, I'd probably run Gemini first for strategic insight, then validate with GPT's stats if needed, then use Claude's formatting for the final deck.
[Full breakdown with actual response screenshots](https://www.khons.co/blog/best-ai-for-data-analysis-2025-gpt-5-1-vs-gemini-3-vs-claude-opus-4-5-tested)
Anyone else running these models on actual messy datasets? Curious what you're seeing on more technical stuff like time series or cohort analysis, my tests were maybe more reasoning heavy.
--- TOP COMMENTS ---
Cleverly designed marketing post for your blog. The odd casual wording was made to look organic, and the engagement bait at the end was meant to drive the algorithm for views, while looking like you genuinely wanted others experiences. (You don't, you want views)
No one trusts this post. It's not real.
Thumbs DOWN
---
Just a general note for anyone reading (not a criticism of this OP in particular): posts like this are usually closer to anecdotal product impressions than to an actual experiment or study. The prompts are often hand-picked, the number of trials is small, and there is no real control or statistical analysis, so the results should not be treated as evidence that "model X is better than model Y." They can still be useful for seeing example prompts and how different models "feel" to use, but it is best to treat the linked blog as marketing plus personal experience, not as rigorous evaluation. That is usually best-case; worst-case it is just advertising uBlock can't detect...
Yesterday
AI solved an open math problem!
We are on the cusp of a profound change in the field of mathematics. Vibe proving is here.
Aristotle from HarmonicMath just proved Erdos Problem #124 in leanprover, all by itself. This problem has been open for nearly 30 years since conjectured in the paper “Complete sequences of sets of integer powers” in the journal Acta Arithmetica.
Boris Alexeev ran this problem using a beta version of Aristotle, recently updated to have stronger reasoning ability and a natural language interface.
Mathematical superintelligence is getting closer by the minute, and I’m confident it will change and dramatically accelerate progress in mathematics and all dependent fields.
**Source: @vladtenev**
--- TOP COMMENTS ---
This is nothing like "mathematical superintelligence". This is a massive misunderstanding and wild over-selling about what Lean is and what was accomplished here.
Lean is a *proof assistant* which is a piece of software that provides a language for defining objects, specifying properties of these objects, and proving that these specifications hold.
It is NOT some general sort of "AI".
The real work here is taking the problem statement and having a HUMAN turn it into a Lean specification that rigorously described the problem. This is not anything like "Vibe" proving.
People have been using Lean to develop proofs for several years now.
[https://leanprover-community.github.io/](https://leanprover-community.github.io/)
[https://en.wikipedia.org/wiki/Lean\_(proof\_assistant)](https://en.wikipedia.org/wiki/Lean_(proof_assistant))
We are seeing this a lot on popular AI forums these days. A group working with some type of automated reasoning tool makes an accomplishment and (funding and press being needed by everyone these days) toutes it as "AI" and chatbot fans assume that this is an LLM showing new autonomous thinking capability jumping ahead of humans when LLMs are not even involved.
---
https://www.reddit.com/r/math/comments/1paewg5/aristotle_from_harmonic_just_proved_erdos_problem/..
Aristotle from HarmonicMath just proved Erdos Problem #124 in leanprover, all by itself. This problem has been open for nearly 30 years since conjectured in the paper “Complete sequences of sets of integer powers” in the journal Acta Arithmetica.
Boris Alexeev ran this problem using a beta version of Aristotle, recently updated to have stronger reasoning ability and a natural language interface.
Mathematical superintelligence is getting closer by the minute, and I’m confident it will change and dramatically accelerate progress in mathematics and all dependent fields.
**Source: @vladtenev**
--- TOP COMMENTS ---
This is nothing like "mathematical superintelligence". This is a massive misunderstanding and wild over-selling about what Lean is and what was accomplished here.
Lean is a *proof assistant* which is a piece of software that provides a language for defining objects, specifying properties of these objects, and proving that these specifications hold.
It is NOT some general sort of "AI".
The real work here is taking the problem statement and having a HUMAN turn it into a Lean specification that rigorously described the problem. This is not anything like "Vibe" proving.
People have been using Lean to develop proofs for several years now.
[https://leanprover-community.github.io/](https://leanprover-community.github.io/)
[https://en.wikipedia.org/wiki/Lean\_(proof\_assistant)](https://en.wikipedia.org/wiki/Lean_(proof_assistant))
We are seeing this a lot on popular AI forums these days. A group working with some type of automated reasoning tool makes an accomplishment and (funding and press being needed by everyone these days) toutes it as "AI" and chatbot fans assume that this is an LLM showing new autonomous thinking capability jumping ahead of humans when LLMs are not even involved.
---
https://www.reddit.com/r/math/comments/1paewg5/aristotle_from_harmonic_just_proved_erdos_problem/..
Yesterday
A Technical Tour of the DeepSeek Models from V3 to V3.2
Tl;dr: DeepSeek V3.2, the latest open-weight model release from the DeepSeek team, uses sparse attention and self-verification techniques to improve efficiency and math performance.
Key Takeaways:
- DeepSeek V3.2 is an updated model with a similar architecture to its predecessors, featuring a sparse attention mechanism to improve efficiency.
- The model uses self-verification, a technique where the model checks its own work, to improve math performance.
- DeepSeek V3.2 has several improvements to the training pipeline, including updates to the Group Relative Policy Optimization (GRPO) algorithm.
- The model comes in a variant called DeepSeek V3.2-Speciale, which was trained only on reasoning data and allows for longer responses.
Yesterday
Funding
BREAKING: Anthropic reportedly planning IPO by early 2026, eyeing massive $300B valuation
**Summary of the Report:**
**The Move:** Anthropic has hired law firm Wilson Sonsini to lay the groundwork for an IPO as early as 2026.
**The Valuation:** They are reportedly in talks for a private funding round that would value the company at over **$300 billion.**
*Context:* This is a massive jump from their ~$60B valuation in March 2025 and ~$183B in September.
**The Revenue:** The valuation surge follows reports that *Claude Code* alone is nearing **$1B** in annualized revenue, pushing their total run rate closer to **$9B** by year-end.
**Source: Reuters / FT**
**Link:** https://www.reuters.com/business/retail-consumer/anthropic-plans-an-ipo-early-2026-ft-reports-2025-12-03/
--- TOP COMMENTS ---
You really made the leap from “as early as 2026” to “early 2026” huh
---
Let the enshitification begin!..
**The Move:** Anthropic has hired law firm Wilson Sonsini to lay the groundwork for an IPO as early as 2026.
**The Valuation:** They are reportedly in talks for a private funding round that would value the company at over **$300 billion.**
*Context:* This is a massive jump from their ~$60B valuation in March 2025 and ~$183B in September.
**The Revenue:** The valuation surge follows reports that *Claude Code* alone is nearing **$1B** in annualized revenue, pushing their total run rate closer to **$9B** by year-end.
**Source: Reuters / FT**
**Link:** https://www.reuters.com/business/retail-consumer/anthropic-plans-an-ipo-early-2026-ft-reports-2025-12-03/
--- TOP COMMENTS ---
You really made the leap from “as early as 2026” to “early 2026” huh
---
Let the enshitification begin!..
Yesterday
Hardware
Optimus: Next-Generation Highly Flexible Hand
--- TOP COMMENTS ---
---
I know we are all thinking the same thing......
Yesterday
Applications
DesignPrompts - I used Opus 4.5 to generate 30 "actually good" design styles/pages, all from the same dataset. Copy-paste the prompts.
I've been genuinely amazed by how good Opus 4.5 is at design, and I built something I think the Claude community might enjoy — [**designprompts.dev**](https://designprompts.dev).
It’s a **gallery of 30+ distinct AI-generated web design styles**, all created using **Claude Opus 4.5**. Every page is built from the **exact same dataset** (`data.json`) — meaning the content never changes, only the design system does.
The idea was inspired by CSS Zen Garden — an old (and hopefully still popular today) site where designers styled the same HTML in hundreds of ways to show what was possible with pure CSS. This is the same concept, but for **AI prompts instead of CSS**.
Each style is:
* **"Actually good and does not look like AI slop"** (quote from my designer friend lol)
* **Optimized prompt**. This was a month+ project that started even before Opus's release, and the prompts went through many iterations to make sure that a new AI session can produce quality designs with the prompts as the input.
* 100% **free and open**, no login or paywall
You can browse all 30+ styles, copy the underlying prompt, and use it directly in Claude to get strong, consistent UI generations in that design language.
Would love feedback on:
* Which design styles feel most production-ready
* How the prompts could be improved for usability
* What styles you’d like to see added next
This is both a showcase of how good Opus 4.5 is at design, and also a useful resource for others to easily apply these styles to their own apps or sites. Enjoy!
--- TOP COMMENTS ---
super slick site - clicked through each and definitely going to play around with generating non-purple vizs
---
This is fantastic. Thanks so much for sharing. I think the designs look really good...
It’s a **gallery of 30+ distinct AI-generated web design styles**, all created using **Claude Opus 4.5**. Every page is built from the **exact same dataset** (`data.json`) — meaning the content never changes, only the design system does.
The idea was inspired by CSS Zen Garden — an old (and hopefully still popular today) site where designers styled the same HTML in hundreds of ways to show what was possible with pure CSS. This is the same concept, but for **AI prompts instead of CSS**.
Each style is:
* **"Actually good and does not look like AI slop"** (quote from my designer friend lol)
* **Optimized prompt**. This was a month+ project that started even before Opus's release, and the prompts went through many iterations to make sure that a new AI session can produce quality designs with the prompts as the input.
* 100% **free and open**, no login or paywall
You can browse all 30+ styles, copy the underlying prompt, and use it directly in Claude to get strong, consistent UI generations in that design language.
Would love feedback on:
* Which design styles feel most production-ready
* How the prompts could be improved for usability
* What styles you’d like to see added next
This is both a showcase of how good Opus 4.5 is at design, and also a useful resource for others to easily apply these styles to their own apps or sites. Enjoy!
--- TOP COMMENTS ---
super slick site - clicked through each and definitely going to play around with generating non-purple vizs
---
This is fantastic. Thanks so much for sharing. I think the designs look really good...
Yesterday
Healthify upgrades its AI assistant Ria with real-time conversation capabilities
Tl;dr: Healthify, a health tracking company backed by Khosla, has launched an updated AI assistant Ria that allows live conversations for better health insights. Users can log meals, workouts, and glucose levels and get personalized suggestions.
Key Takeaways:
- Healthify has launched a new AI assistant Ria that can converse live with users using voice or camera input.
- Ria can support multiple languages, including 14 Indian languages, and also supports mixed language input.
- The AI assistant can pull data from various sources like fitness trackers, sleep trackers, and glucose monitors.
- Users can ask Ria for health insights, generate an exercise plan, or log their goals, making the app a central piece of user onboarding.
Yesterday
Regulation
AI governance: What it is and why it's crucial for every business
Tl;dr: AI governance encompasses the principles and practices to ensure AI is developed and used responsibly, ethically, and securely.
Key Takeaways:
- AI governance sets the rules for how AI is used in an organization, including policies and best practices for AI tool selection, employee training, data management, and risk management.
Yesterday
Opinion And Analysis
AI has redefined the talent game. Here’s how leaders are responding.
Tl;dr: Tech job postings are down 30% from pre-pandemic highs yet demand for AI expertise is greater than ever, prompting companies to rethink talent strategies.
Key Takeaways:
- Tech companies are reducing job postings by 30% from pre-pandemic levels due to AI-driven changes in work, yet AI expertise demand is increasing.
- Organizations are introducing new roles, such as prompt engineers and AI operations managers, to address the growing need for AI expertise.
- Leaders are focusing on finding the right candidates, and onboarding and upskilling talent in AI-related skills to meet business needs.
Yesterday