← Back to leaderboard
alex_whedon

Alexander Whedon

Verifiedactive
@alex_whedon

Building better algorithms. Co-Founder, CEO at @subquadratic

26,203 followers · 85 following · Last polled 1h ago
Perf. Score
36.9
Rank #318
Value Score
—
set rates to compute
Median views
1.1K
p25 810 · trailing 7d
Followers
26K
+0.0% 7d
ER (impr.)
1.81%
eng ÷ impressions
ER (foll.)
0.09%
eng ÷ followers
Est. CPM
—
needs a rate
WoW views
-99.8%
▼ falling

Rates & value · est. from 1.1K median organic views

Quote tweet
—
no CPM
Post
—
no CPM
Thread
—
no CPM
Retweet
—
no CPM
rate age unknown (pre-audit import)

Trends

Follower growth
Median views / day
Engagement rate
View distribution · trailing-7d posts

Each blue dot is one organic post. The Performance Score ranks by the median (1.1K), not the mean (1.2K) — so a single viral post doesn't inflate a creator's reach. The p25 floor (810) is the level 75% of posts clear.

Recent posts · ranked by views

PostPostedViewsLikesRepostsRepliesQuotesBookmarksER
Introducing SubQ Snap, the first long-context decision model for real enterprise work. Snap is the only decision model or LLM where compute per token stays constant as context grows. • 384K tokens of multimodal context, 12x larger than Jev. • 61.7 on Decision Index v0.2.1 vs Jev’s 57.9, outperforming on 13 of 16 enterprise benchmarks. •Processes 100K tokens and returns a decision in under one second. •$0.05/MM input tokens. Output is free. Available today to anyone. The first 10,000 sign-ups get 100M free input tokens. We’ll likely hit capacity fast and will add users as we add compute. Compute offers welcome 🙂. Latency will be variable as we tune the platform to this new model and category of users. Model speeds are 80ms for minimal context, 250ms for 32k, 660ms for 100k, and 2.5 seconds for 384k. Visit: https://t.co/Nc76iJF2a8frozen10d ago4.5M3.1K6475762673.2K0.17%
We hit 11.4B tokens in the last hour and nearly 7B in the last half hour, within the first three hours after launch! You guys are cooking! Thanks for the engagement. It is possible there may be some latency spikes. We are considering opening up some of our training clusters for inference. We will let you know if anything changes!
frozen
10d ago
11K
67
5
9
3
9
0.84%
We use SkyPilot to help manage our multi-cloud GPU clusters for training and inference. The team goes above and beyond to address our niche feature requests as we address problems other labs don’t face. Thanks for helping make this launch a success!frozen10d ago3.8K4054061.47%
Yesterday, we released Snap, the first fully sub-quadratic language model that is generally available and in production at scale, and it is consistently winning at the frontier of decision model space in the real world, and in prod! Transformers have inverse economies of scale. As the input size grows, the compute you need even on a per-token basis also grows. 10x the input requires 100x the attention compute. Our model has constant compute-per-token no matter what the input size is, during both training and inference. This enables us to make smarter models with divergent capabilities and economics, no matter what the model category is. The community response so far has been amazing! Here are some of the cool things we have seen people build: - coding agent memory - UI design - recommendation systems - legal contract review - entity recognition - academic research curation - bug finder - etc. We can't wait to see what you build next with Snap.frozen9d ago3.7K5958392.29%
SubQ Snap does well in what TypeSafe calls the "Danger Zone", context above 32k. 85% of tokens processed by Snap are from requests above 32k, with many shipping data-intensive features to prod. SSA keeps long context fast and cheap. We are open-sourcing Decision LCR (Snap 74%, Jev 58%), which mimics real-world tasks and is adapted from AA-LCR. Because long-context benchmarks for decision models don't really exist, we built one of our own by adapting Artificial Analysis' Long-Context Reasoning benchmark, which we think does a better job mimicking real-world tasks than many LLM long-context benchmarks, to a decision format. We are open-sourcing this today. Decision LCR: https://t.co/JGGSEBBczF Adapted from Artificial Analysis's AA-LCR. 100 human-written questions over real 72K–115K-token document sets, converted into 118 decisions, each run with three option orderings to control for position bias. Chance is 19.2, and models score 24–25 with the documents removed. We'd love for others to run it, use it and improve it. If you work on long-context evaluation or decision models, we'd value your feedback and collaboration. To learn more about SNAP and our evaluation process, check out the blog: https://t.co/CglHueOxnvfrozen7d ago3.4K71872283.36%
This is a great thread from Jon. Jon and CRED were the first to not only test Snap but ship it to prod, and it took them only an HOUR! These results were exciting to us, because we’ve been doing a lot of work in the finance space with our LLM. Snap was equal to or better than Jev on six out of seven use cases while having better probability calibration (common feedback). The different was even bigger against the OpenAI Decisions API. More on this exploration together is coming soon!frozen10d ago2.6K2610021.11%
Jake's analyses are always thorough and intriguing! Thanks for benchmarking our model!frozen10d ago2.3K2303001.14%
This is a great evaluation of how we expect decision models to be used: doing real work, on data-intensive workloads. Snap outperforms Jev within the supported context, but performance improves when the context window is leveraged beyond 32k! We expect to see decision models doing more context curation for LLMs.frozen10d ago2.3K1811041.05%
It is awesome to see @RichOBray using Snap to cut their Claude bill 74% in this @Firecrawl model picker! Check out the tutorial here: https://t.co/9ZFXpPSPpM Model-picker Github: https://t.co/wv5l40J3Gk... https://t.co/VTYjpCIK9j1d ago2.1K2151131.49%
Grateful for this group of radicals! and dessert.frozen27d ago1.5K1411021.18%
https://t.co/a4AG32vX4N2d ago1.1K1202011.32%
Today we're introducing the smartest, most capable version of our enterprise-ready decision model: SubQ Snap 1.1. Snap is tied for #1 on the Artificial Analysis Decision Index and has made big gains on agentic tasks, outperforming Jev by +7.2 points across 16 leading enterprise benchmarks. All while maintaining 384k tokens of context, in a single pass, as the only model with constant compute per token. - Powered by Subquadratic Sparse Attention. - Tied for #1 on the @ArtificialAnlys Decision Index. - 96.2 on When2Call, up from 75.2, and up 6 points on @huggingface 5 Tools & Automation benchmarks. It knows when to call a tool, when to ask, and when to decline. - 81% fewer confident errors on questions it can't answer. - $0.04 per 1M input tokens. No charge for output. Go to https://t.co/v454Mk1Lxd for access, and 100M free tokens.1h ago4821032334.36%