Huku AI

A small product lab building AI tools for deep thinkers. Tools that respect your attention and augment your thinking | HU-man K-nowledge & U-You

[Huku AI @HukuLabs]

We just want to build quality software and have fun !

[Huku AI @HukuLabs]

Checkout our Full Blog for deep dive
https://t.co/UQse0nSUP5

[Huku AI @HukuLabs]

1/ A deep dive into why reasoning models (O1, DeepSeek) represent a fundamental shift in AI interaction. From my experience working with these models, here's what you need to know about maximizing their potential 🧵

[Huku AI @HukuLabs]

We're moving from "fast and iterative" to "thoughtful and comprehensive" AI interaction. The future of AI interaction may be high-latency but high-quality. What seems like a limitation—taking more time to process—becomes a strength for tackling complex analytical tasks.

[Abhinav Anand @ab_anand10]

"I am gonna eat you all" says OpenAI

OpenAI just made a massive entry into consumer game with the launch of Mac App last week. We all focused on the ‘o’ in the GPT-4o, but OpenAI just changed the whole landscape. A mac app, that sees it all, understands it all and maybe in future acts on all, is the mother lord of the new generation of LLM based OS.

Till the time Apple’s Siri comes out of it’s slumber and realises what is expected of a voice based assistant, it gives good enough time for OpenAI to run wild and build a solid consumer presence with it’s conversational agent offerings across all platforms.

Everybody thought OpenAI primary strategy is to be a developer friendly AI cloud service provider. They were first and most powerful LLM provider in the market. OpenAI’s entry into consumer game, just highlights the fact that their is gonna be very low value in just building world-class models (the reason https://t.co/LYAvJ3oC46 had to be absorbed into Microsoft rather than competing in this fierce low-margin model serving business). Maybe a few top niche model providers like ElevenLabs have a chance, but most of it is gonna get commoditised very soon and offered at dirt cheap price on AWS.

OpenAI’s mac-app and voice-based human interaction suddenly puts them on a head-on collision path with Google and Apple, maybe even their pals at Microsoft should now get a bit worried.

Be warned and cautious, OpenAI is not just coming for Google and Apple, they are coming after all language translation apps be it DuoLingo, they are coming after all edTech products, they are gonna steamroll all the chatgpt wrappers, they are gonna kill every character building AI startups, almost anything related to language is in the firing line.

Nobody is gonna pay for multiple AI service providers, it’s gonna be winner takes all market, similar to Facebook in social commerce or Apple in phone commerce. Very few AI services would be alive when the dust settles. SaaS, Business process automation and other niche use-cases like generative AI editing tools are a different category but consumer facing generic image generation, language related AI use-cases are gonna soon fall under a single OpenAI banner

So, beware and stay as far as possible yet navigating around OpenAI’s ring of fire, because that’s where the new generation of Software is gonna be rewritten so you just cannot avoid OpenAI.

We have an interesting coverage of all the interesting features of OpenAI GPT-4o model on our website. Do take a quick read.

#openai #gpt4o

https://t.co/cLMzVGeop6

[Huku AI @HukuLabs]

RT @ab_anand10: The Two Flavours of B2C SaaS Products: B2C2B vs B2B2C

When you consider building a SaaS product for consumers, be clear to…

[Mrigesh @parasharmrigesh]

Hume’s Empathic Voice Interface (EVI), the first conversational AI with emotional intelligence is the bold step forward for human-machine interactions.
It feels natural and, at times, magical!

Checkout my recording:
https://t.co/CbDTzBILiQ

[Abhinav Anand @ab_anand10]

Software Development is changing and massively: Devin

@cognition_labs Devin is not a technological breakthrough but a product breakthrough. It clearly shows how to design AI native tools. AI is gonna be in everything we do. So just a editor and co-pilot doesn’t cut it.

Bring all my developer tools at one place. Editor, Console, Browser, Deploy all at one place each powered by our favourite friend OpenAI in its own way. Editor assists you editing, Console assists you in debugging, Browser assists you in researching, Deploy assists you making your product live.

What else do you need? Devin goes on top and provides you a Planner, an AI Tech Lead is now always part of the development process.

Planning has existed for a while, projects like Autogpt, BabyGPT, Camel have been working on it for a while, but using it all in one tool and in a seamless way creates magic

Understand this is the new avatar of “VSCode”. Developers should learn it and become 4X more efficient at their work.

https://t.co/en4ycmRCEp

#Devin #devinai #Cognitionlabs #cognition

[Abhinav Anand @ab_anand10]

Product management is less about what features to create rather more about what features to discard.

Adding more features satisfies all the stakeholders involved but always leads to a non-optimal output. Mixed veggies never caught people’s taste buds as much as Butter chicken. So you understand more ingredients doesn’t make the food taste better.

At the same time, more features requires more design time, more build time and just add the time required to remove all the unused lines of code and the complexity it adds for all the future product building.

Optimal solution needs an extreme focus on the utility of the product and separate it from all the features and feature addiction.

Software development needs to learn how to get out of this feature addiction. Sometimes a simpler solution might be a better solution (Remember Occam’s Razor).

Probably with LLMs we might lead to a new interface where you just end up having only one button to get everything done at one click.

#product #startups #ai #llm #aiinterfaces #minimalistengineering

[Mrigesh @parasharmrigesh]

Matrix math is so important to AI that even slight gains in efficiency, makes them more accessible by reducing computation
Matrix calculation and Fourier transform are the perfect example how fundamental research in math leads to changing world.

https://t.co/LB3uQe79ZG

[Huku AI @HukuLabs]

TL;DR

Problem: LLM needs intensive memory requirements
which makes it challenging to run it locally on everyday devices
- Apple have developed a method to efficiently run LLMs on devices with limited DRAM.
- Instead of relying on DRAM, the method uses flash memory for storing model parameters.
- Introduces 'windowing' and 'row-column bundling' to operate models up to twice the DRAM size,
- Inference speeds are greatly enhanced - 4-5 times faster on CPUs and 20-25 times on GPUs.

[Huku AI @HukuLabs]

Apple's New Technique for Powerful AI on Regular Devices: LLM in a Flash

Apple has developed a clever method to run advanced LLMs, which usually requires powerful computers, on everyday devices like smartphones and tablets. Large language models (LLMs), typically need a lot of dynamic memory to work or DRAM. However, many devices don't have enough memory to handle them.

Apple's solution is to store parts of the LLM's in a different kind of memory, similar to a USB flash drive, and only use the device's main memory when absolutely necessary. They use two main strategies to make this efficient: one is like recycling information so the AI doesn't constantly ask for new data, and the other is about fetching data in fewer, but larger, batches. This approach allows devices to run AI programs that are bigger than their own memory limits and do it much faster than before.
This breakthrough greatly broadens the possibilities for using sophisticated large language models in settings with limited resources. It's a significant step in making powerful LLMs accessible on the devices and applications we use every day.
https://t.co/9Bdy0Wr0ui

[Huku AI @HukuLabs]

To read more similar in-depth analysis on fast evolving AI landscape, do subscribe to @HukuLabs. We share in-depth coverage on AI topics.

#Google #googlegemini #ChatGPT

[Huku AI @HukuLabs]

Overall there should be no battle between what’s the right prompting technique or evaluation standards. The most relevant point to focus is how to help LLMs perform the task at hand well. As we see here using “Chain of Thought” prompting does improve model output on various reasoning problems compared to n-shot prompting.