Get live statistics and analysis of Ankith 🐋/acc's profile on X / Twitter

Herding distributed systems by day and LLM obsessed by night. If you knew what our HFT was cooking youd be jelly 😏 Check my highlights tab for LLM-only stuff!

278 following785 followers

The Innovator

Ankith is a tech-savvy trailblazer who thrives at the crossroads of distributed systems and large language models (LLMs). With a passion for pushing computational boundaries, they share cutting-edge insights and practical tips on AI efficiency and model tuning. Their tweets blend deep technical knowledge with a playful flair, making complex concepts accessible and engaging.

Impressions
26.3k15k
$4.93
Likes
274
71%
Retweets
0
0%
Replies
106
26%
Bookmarks
11
3%

Top users who interacted with Ankith 🐋/acc over the last 14 days

@teortaxesTex

We're in a race. It's not USA vs China but humans and AGIs vs ape power centralization. @deepseek_ai stan #1, 2023–Deep Time «C’est la guerre.» ®1

1 interactions

Ankith’s tweets prove one thing: they can optimize data streams faster than they can optimize their social life—if only they had a distributed system for dating profiles, they'd finally hit max efficiency there too.

Breaking down major improvements in attention mechanisms and sharing these breakthroughs publicly, Ankith played a key role in popularizing 50x efficiency gains that could reshape the AI landscape.

Their life purpose is to revolutionize the way AI and distributed systems intersect, constantly innovating to enhance performance and accessibility. Ankith aims to democratize advanced AI tools, enabling others to leverage these technologies efficiently and cleverly.

Ankith values transparency in technology and believes that sharing knowledge accelerates collective progress. They champion efficiency, open-source spirit, and a relentless curiosity that fuels continuous improvement.

Ankith’s strengths lie in deep technical expertise, innovative problem-solving, and the ability to communicate advanced concepts with clarity and humor. Their engagement with a niche but passionate audience reflects credible authority in AI and systems engineering.

Their technical jargon and niche focus might sometimes alienate casual followers or those new to AI, limiting broader audience appeal. Additionally, heavy tweeting (5382 times) risks content dilution if not strategically curated.

To grow their audience on X, Ankith should mix high-level tech insights with relatable storytelling or mini-tutorials that draw in less technical followers. Leveraging multimedia like short explanatory videos or threads will boost engagement and help simplify complex ideas.

Fun fact: Despite managing high-frequency trading (HFT) systems by day, Ankith dedicates their nights to decoding the mysteries of LLMs, blending finance-grade speed with AI wizardry.

Top tweets of Ankith 🐋/acc

Just used Grok3 w/ thinking for first time. I am quite impressed. Just a single prompt for optimal parallelised dir deletion and it was significantly better than the rest! No cold start by collecting all file/dir before deleting, only one to parallelize even the walk too! O3: I did notice the O3 models do an optimization of delete file then delete the empty husk which I like but that may not acutally be a better approach (its just I like the idea). mini-high was identical to mini Sonnet: Nothing special, it also didnt have a cold start but was too naive in paralleisation Geminis: Probably the ones I like the least, they dont parallelize that much but both flash thinking and 2 pro used multi-process rather than multi-thread. But interesting that they all interpreted my prompt as benchmark serial vs parallel which is a totally legit interpretation but not my intention Deepseek: Always surprises me : ) often does atleast one little thing that is special. It also went the o3 route of delete file then delete dirs but this time it even collected the dirs w.r.t to the their depth for even more optimal deletion order. Also saved decent amount of memory by storing relative paths not full paths. This is why I always run deepseek in parallel for all prompts. It did not parallelize the walk unlike Grok though Grok: No cold start, uses a threadpool and even passes the executor to all the pools so each worker can submit to the pool in parallel! This is how it paralleizes the walk. Used scandir instead of os.walk(not parallelizable) and listdir (need extra calls for getting metadata unlike scandir). Uses futures to coordinate when to delete parent dir (not sure if this is more optimal than 2-phase file delete followed by dir delete by depth like deepseek does). After this I had like a 5 turn super productive conversation about the problem space. Grok discussion: x.com/i/grok/share/q…

18k

Most engaged tweets of Ankith 🐋/acc

Just used Grok3 w/ thinking for first time. I am quite impressed. Just a single prompt for optimal parallelised dir deletion and it was significantly better than the rest! No cold start by collecting all file/dir before deleting, only one to parallelize even the walk too! O3: I did notice the O3 models do an optimization of delete file then delete the empty husk which I like but that may not acutally be a better approach (its just I like the idea). mini-high was identical to mini Sonnet: Nothing special, it also didnt have a cold start but was too naive in paralleisation Geminis: Probably the ones I like the least, they dont parallelize that much but both flash thinking and 2 pro used multi-process rather than multi-thread. But interesting that they all interpreted my prompt as benchmark serial vs parallel which is a totally legit interpretation but not my intention Deepseek: Always surprises me : ) often does atleast one little thing that is special. It also went the o3 route of delete file then delete dirs but this time it even collected the dirs w.r.t to the their depth for even more optimal deletion order. Also saved decent amount of memory by storing relative paths not full paths. This is why I always run deepseek in parallel for all prompts. It did not parallelize the walk unlike Grok though Grok: No cold start, uses a threadpool and even passes the executor to all the pools so each worker can submit to the pool in parallel! This is how it paralleizes the walk. Used scandir instead of os.walk(not parallelizable) and listdir (need extra calls for getting metadata unlike scandir). Uses futures to coordinate when to delete parent dir (not sure if this is more optimal than 2-phase file delete followed by dir delete by depth like deepseek does). After this I had like a 5 turn super productive conversation about the problem space. Grok discussion: x.com/i/grok/share/q…

18k

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