Pinned Tweet
Help cut your AI costs with models built for the task!
Run 100+ task-specific models behind one production API.
Check out SIE Serverless in @svonava 's post below.
Run 100+ task-specific models behind one production API.
Check out SIE Serverless in @svonava 's post below.
🚨 Huge news 🚨 today we are launching SIE Serverless - a managed inference product focused on small models. It’s useful for spiky workloads on 20+ task specific workloads across Search, OCR and Agentic execution.
Sign up for a $500 credits launch grant 👇
It’s powered by our ngine that has received thousands of github stars in the last few months, battle-tested across finance, legal & tech. We believe that self-hosting is going to be critical given all the trust & security issues in AI, but practically there are always workloads where you’d like somebody else to deal with the GPU quota like synthetic data generation, evals, public research and beyond.
The team at Superlinked has been burning the midnight oil for months and I’m so proud of every single one of you - what a privilege to call you my team mates y’all!
We built it, now help us make some noise 🙌
A like, a comment or a repost would mean a lot! ❤️
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Superlinked retweeted
Want to try open weight Task Specific models but don't know where to start? @superlinked has offering a grant of 500$ of credits to try them! Would love for you to check them out.
So the launch went well, we have received 265 grant applications and have many more accounts with real usage 🤗 Chart up to the right! :-)
If you are not sure if SLMs can do the job, here is a little inspiration - a summary of real projects already building on the platform 💪
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Superlinked retweeted
Fast inference for Small Language Models (@svonava) is the engine behind Compute-Optimal agentic scaling: once sampling is cheap, parallel search beats raw parameter count.
Stanford CS329A: Self-Improving AI Agents · Study Notes 03 (Pass@k Power Laws & Coverage)
• Pass@k Scale math, 1 - (1 - p)^k, so a model with a 10% single-shot pass rate reaches 65% coverage in 10 tries.
• Best-of-N shifts the Compute-Optimal frontier: on the same inference budget, a 7B model sampling 64 parallel drafts outperforms a 70B model at Pass@1.
• Small Language Models win on spiky agentic workloads when high-temperature sampling produces diverse candidates instead of 64 near-identical copies.
• Verifier-Guided Search turns coverage into final accuracy, since candidate generation only helps as far as your verifier can pick the winning rollout.
Stanford CS329A: Self-Improving AI Agents · Study Notes 03 (Pass@k Power Laws & Coverage)
• Pass@k Scale math, 1 - (1 - p)^k, so a model with a 10% single-shot pass rate reaches 65% coverage in 10 tries.
• Best-of-N shifts the Compute-Optimal frontier: on the same inference budget, a 7B model sampling 64 parallel drafts outperforms a 70B model at Pass@1.
• Small Language Models win on spiky agentic workloads when high-temperature sampling produces diverse candidates instead of 64 near-identical copies.
• Verifier-Guided Search turns coverage into final accuracy, since candidate generation only helps as far as your verifier can pick the winning rollout.
🚨 Huge news 🚨 today we are launching SIE Serverless - a managed inference product focused on small models. It’s useful for spiky workloads on 20+ task specific workloads across Search, OCR and Agentic execution.
Sign up for a $500 credits launch grant 👇
It’s powered by our ngine that has received thousands of github stars in the last few months, battle-tested across finance, legal & tech. We believe that self-hosting is going to be critical given all the trust & security issues in AI, but practically there are always workloads where you’d like somebody else to deal with the GPU quota like synthetic data generation, evals, public research and beyond.
The team at Superlinked has been burning the midnight oil for months and I’m so proud of every single one of you - what a privilege to call you my team mates y’all!
We built it, now help us make some noise 🙌
A like, a comment or a repost would mean a lot! ❤️
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Insanely good numbers...
Run the models and see the results for yourself: auth.superlinked.com/sign-up
Run the models and see the results for yourself: auth.superlinked.com/sign-up
"YOU NEED A BIG BOLD CLAIM IN YOUR LAUNCH"
So anyway, the evals we used for this and how robust are the findings, see: superlinked.com/blog/launch
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Superlinked retweeted
"YOU NEED A BIG BOLD CLAIM IN YOUR LAUNCH"
So anyway, the evals we used for this and how robust are the findings, see: superlinked.com/blog/launch
So anyway, the evals we used for this and how robust are the findings, see: superlinked.com/blog/launch
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Superlinked retweeted
Your AI agent might be using a $100 solution for a 10-cent problem.
Not every task needs your most powerful model.
Classification, OCR, extraction, reranking and guardrails can often run on smaller, more cost-efficient models.
That's the problem Superlinked SIE is solving.
behind your agent.
Keep frontier intelligence for frontier work.
Not every task needs your most powerful model.
Classification, OCR, extraction, reranking and guardrails can often run on smaller, more cost-efficient models.
That's the problem Superlinked SIE is solving.
behind your agent.
Keep frontier intelligence for frontier work.
🚨 Huge news 🚨 today we are launching SIE Serverless - a managed inference product focused on small models. It’s useful for spiky workloads on 20+ task specific workloads across Search, OCR and Agentic execution.
Sign up for a $500 credits launch grant 👇
It’s powered by our ngine that has received thousands of github stars in the last few months, battle-tested across finance, legal & tech. We believe that self-hosting is going to be critical given all the trust & security issues in AI, but practically there are always workloads where you’d like somebody else to deal with the GPU quota like synthetic data generation, evals, public research and beyond.
The team at Superlinked has been burning the midnight oil for months and I’m so proud of every single one of you - what a privilege to call you my team mates y’all!
We built it, now help us make some noise 🙌
A like, a comment or a repost would mean a lot! ❤️
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Superlinked retweeted
Each agent startup after seeing the inference invoice:
🚨 Huge news 🚨 today we are launching SIE Serverless - a managed inference product focused on small models. It’s useful for spiky workloads on 20+ task specific workloads across Search, OCR and Agentic execution.
Sign up for a $500 credits launch grant 👇
It’s powered by our ngine that has received thousands of github stars in the last few months, battle-tested across finance, legal & tech. We believe that self-hosting is going to be critical given all the trust & security issues in AI, but practically there are always workloads where you’d like somebody else to deal with the GPU quota like synthetic data generation, evals, public research and beyond.
The team at Superlinked has been burning the midnight oil for months and I’m so proud of every single one of you - what a privilege to call you my team mates y’all!
We built it, now help us make some noise 🙌
A like, a comment or a repost would mean a lot! ❤️
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Superlinked retweeted
bills will continue until morale improves (or small models get deployed) ^_^
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Superlinked retweeted
SuperExcited for @superlinked 's launch! For the past month, I have been 'in-residency' at Superlinked during my sabbatical. Here's the deets!
a) You don't want to always want to be managing the model containers in production, bringing them up and tearing them down with simple yaml configurations. You want a battle tested engine that can autoscale the nodes based on what's actively being asked and load balance the requests esp. when they take a lot of time serving tokens for LLMs.
b) Not everything has to be an LLM call! Specialized models are thriving and you can use one of the many open-weights models that are task specific to do the same work faster, cheaper and more effectively. Wrap them in tool calls or make them part of your deterministic workflows!
c) Running models on your own infrastructure costs a lot! SIE helps reduce your spend by having model eviction and fractionally using the GPUs by putting multiple SLMs on a single one. This allows for a much more leaner stack. With SIE serverless in the cloud, you get the same benefits and allows you to try these models before you start adapting these and serving them on your own infrastructure.
The residency is allowing me a look into how one of the sharpest teams runs production grade inference models. At the same time, I get to talk to developers and contribute to the Open Source AI ecosystem. I'm collaborating on hosting a few events in NY, SF, Online + attending Kubecon, and more! Looking forward to the chat!
a) You don't want to always want to be managing the model containers in production, bringing them up and tearing them down with simple yaml configurations. You want a battle tested engine that can autoscale the nodes based on what's actively being asked and load balance the requests esp. when they take a lot of time serving tokens for LLMs.
b) Not everything has to be an LLM call! Specialized models are thriving and you can use one of the many open-weights models that are task specific to do the same work faster, cheaper and more effectively. Wrap them in tool calls or make them part of your deterministic workflows!
c) Running models on your own infrastructure costs a lot! SIE helps reduce your spend by having model eviction and fractionally using the GPUs by putting multiple SLMs on a single one. This allows for a much more leaner stack. With SIE serverless in the cloud, you get the same benefits and allows you to try these models before you start adapting these and serving them on your own infrastructure.
The residency is allowing me a look into how one of the sharpest teams runs production grade inference models. At the same time, I get to talk to developers and contribute to the Open Source AI ecosystem. I'm collaborating on hosting a few events in NY, SF, Online + attending Kubecon, and more! Looking forward to the chat!
🚨 Huge news 🚨 today we are launching SIE Serverless - a managed inference product focused on small models. It’s useful for spiky workloads on 20+ task specific workloads across Search, OCR and Agentic execution.
Sign up for a $500 credits launch grant 👇
It’s powered by our ngine that has received thousands of github stars in the last few months, battle-tested across finance, legal & tech. We believe that self-hosting is going to be critical given all the trust & security issues in AI, but practically there are always workloads where you’d like somebody else to deal with the GPU quota like synthetic data generation, evals, public research and beyond.
The team at Superlinked has been burning the midnight oil for months and I’m so proud of every single one of you - what a privilege to call you my team mates y’all!
We built it, now help us make some noise 🙌
A like, a comment or a repost would mean a lot! ❤️
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Superlinked retweeted
Stop overpaying for agents, folks!
A single agent request can trigger many model calls. Your agent might classify a ticket, rerank results, extract a date and run a guardrail before it ever answers.
Sending all of those calls to a frontier model gets expensive fast. And it’s unnecessary.
@superlinked SIE gives you one API for the open models behind those steps.
Across 14 task tests, open models on SIE reached a median 98% of GPT-6.1 Sol's score at 3% of its cost, at list pricing.
Keep the frontier model for the steps that need it, like those requiring complex reasoning.
A single agent request can trigger many model calls. Your agent might classify a ticket, rerank results, extract a date and run a guardrail before it ever answers.
Sending all of those calls to a frontier model gets expensive fast. And it’s unnecessary.
@superlinked SIE gives you one API for the open models behind those steps.
Across 14 task tests, open models on SIE reached a median 98% of GPT-6.1 Sol's score at 3% of its cost, at list pricing.
Keep the frontier model for the steps that need it, like those requiring complex reasoning.
🚨 Huge news 🚨 today we are launching SIE Serverless - a managed inference product focused on small models. It’s useful for spiky workloads on 20+ task specific workloads across Search, OCR and Agentic execution.
Sign up for a $500 credits launch grant 👇
It’s powered by our ngine that has received thousands of github stars in the last few months, battle-tested across finance, legal & tech. We believe that self-hosting is going to be critical given all the trust & security issues in AI, but practically there are always workloads where you’d like somebody else to deal with the GPU quota like synthetic data generation, evals, public research and beyond.
The team at Superlinked has been burning the midnight oil for months and I’m so proud of every single one of you - what a privilege to call you my team mates y’all!
We built it, now help us make some noise 🙌
A like, a comment or a repost would mean a lot! ❤️
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Superlinked retweeted
Signup & apply for the grant (tell us what you are building to help us prioritize): superlinked.com
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Superlinked retweeted
using gpt for "is this a refund request y/n" is like taking a plane to the corner store
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Superlinked retweeted
just saw this and i'm still processing it.
text classification on GPT-6.1 Sol: 1.48 seconds.
same task on SIE: 13.1 milliseconds.
that's over 100x faster.
and your agent doesn't do it once. it does it on every single request.
Superlinked just open-sourced the fix: one API the right model and saves the big one for the hard stuff.
$20 free. no card.
text classification on GPT-6.1 Sol: 1.48 seconds.
same task on SIE: 13.1 milliseconds.
that's over 100x faster.
and your agent doesn't do it once. it does it on every single request.
Superlinked just open-sourced the fix: one API the right model and saves the big one for the hard stuff.
$20 free. no card.
🚨 Huge news 🚨 today we are launching SIE Serverless - a managed inference product focused on small models. It’s useful for spiky workloads on 20+ task specific workloads across Search, OCR and Agentic execution.
Sign up for a $500 credits launch grant 👇
It’s powered by our ngine that has received thousands of github stars in the last few months, battle-tested across finance, legal & tech. We believe that self-hosting is going to be critical given all the trust & security issues in AI, but practically there are always workloads where you’d like somebody else to deal with the GPU quota like synthetic data generation, evals, public research and beyond.
The team at Superlinked has been burning the midnight oil for months and I’m so proud of every single one of you - what a privilege to call you my team mates y’all!
We built it, now help us make some noise 🙌
A like, a comment or a repost would mean a lot! ❤️
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Superlinked retweeted
NOBODY TALKS ABOUT THIS AND IT'S THE BIGGEST BILL IN YOUR AGENT
it's not the llm you picked.
it's that every step of the workflow (classify, rerank, extract, run a guardrail) goes to the same frontier model because it's the easy option.
at scale, most of your inference bill is for work a much smaller model would handle just as well.
Superlinked ships hosted SIE today: one api to pick the right model for each task without rewriting anything.
open source, Apache 2.0, 3.4k stars on github.
repo link below.
it's not the llm you picked.
it's that every step of the workflow (classify, rerank, extract, run a guardrail) goes to the same frontier model because it's the easy option.
at scale, most of your inference bill is for work a much smaller model would handle just as well.
Superlinked ships hosted SIE today: one api to pick the right model for each task without rewriting anything.
open source, Apache 2.0, 3.4k stars on github.
repo link below.
🚨 Huge news 🚨 today we are launching SIE Serverless - a managed inference product focused on small models. It’s useful for spiky workloads on 20+ task specific workloads across Search, OCR and Agentic execution.
Sign up for a $500 credits launch grant 👇
It’s powered by our ngine that has received thousands of github stars in the last few months, battle-tested across finance, legal & tech. We believe that self-hosting is going to be critical given all the trust & security issues in AI, but practically there are always workloads where you’d like somebody else to deal with the GPU quota like synthetic data generation, evals, public research and beyond.
The team at Superlinked has been burning the midnight oil for months and I’m so proud of every single one of you - what a privilege to call you my team mates y’all!
We built it, now help us make some noise 🙌
A like, a comment or a repost would mean a lot! ❤️
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Superlinked retweeted
every agent startup opening the inference bill at the end of the month
🚨 Huge news 🚨 today we are launching SIE Serverless - a managed inference product focused on small models. It’s useful for spiky workloads on 20+ task specific workloads across Search, OCR and Agentic execution.
Sign up for a $500 credits launch grant 👇
It’s powered by our ngine that has received thousands of github stars in the last few months, battle-tested across finance, legal & tech. We believe that self-hosting is going to be critical given all the trust & security issues in AI, but practically there are always workloads where you’d like somebody else to deal with the GPU quota like synthetic data generation, evals, public research and beyond.
The team at Superlinked has been burning the midnight oil for months and I’m so proud of every single one of you - what a privilege to call you my team mates y’all!
We built it, now help us make some noise 🙌
A like, a comment or a repost would mean a lot! ❤️
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Superlinked retweeted
Specialized Models >> Larger Models.
This is an open-source platform that will help you choose and run specialized models in your applications.
Basically, you can set up which models you want to serve specific tasks.
For example, if your agent needs to classify a message, read text on an image, search files, and write a response, you could have 4 different models to serve each of these tasks.
This is an open-source platform that will help you choose and run specialized models in your applications.
Basically, you can set up which models you want to serve specific tasks.
For example, if your agent needs to classify a message, read text on an image, search files, and write a response, you could have 4 different models to serve each of these tasks.
🚨 Huge news 🚨 today we are launching SIE Serverless - a managed inference product focused on small models. It’s useful for spiky workloads on 20+ task specific workloads across Search, OCR and Agentic execution.
Sign up for a $500 credits launch grant 👇
It’s powered by our ngine that has received thousands of github stars in the last few months, battle-tested across finance, legal & tech. We believe that self-hosting is going to be critical given all the trust & security issues in AI, but practically there are always workloads where you’d like somebody else to deal with the GPU quota like synthetic data generation, evals, public research and beyond.
The team at Superlinked has been burning the midnight oil for months and I’m so proud of every single one of you - what a privilege to call you my team mates y’all!
We built it, now help us make some noise 🙌
A like, a comment or a repost would mean a lot! ❤️
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Superlinked retweeted
Most agentic workflows are burning frontier-model money on tasks a small model can handle.
Anthropic researchers found 70% of agent LLM calls could run on small models instead.
so a $30 job turns into a $1000 bill.
That's not a guess.
Across 14 task tests, open models on SIE -6.1 Sol's score at 3% of its cost.
Same 98%. $3 vs $100.
Superlinked just launched hosted SIE:
→ one API for every model behind your agent
→ rerankers, classifiers, OCR, guardrails, LLMs
→ 200+ open models, Apache 2.0
Keep frontier intelligence for frontier work.
Right-size everything else.
try it yourself - get $20 in free credits:
auth.superlinked.com/sign-up
Anthropic researchers found 70% of agent LLM calls could run on small models instead.
so a $30 job turns into a $1000 bill.
That's not a guess.
Across 14 task tests, open models on SIE -6.1 Sol's score at 3% of its cost.
Same 98%. $3 vs $100.
Superlinked just launched hosted SIE:
→ one API for every model behind your agent
→ rerankers, classifiers, OCR, guardrails, LLMs
→ 200+ open models, Apache 2.0
Keep frontier intelligence for frontier work.
Right-size everything else.
try it yourself - get $20 in free credits:
auth.superlinked.com/sign-up
🚨 Huge news 🚨 today we are launching SIE Serverless - a managed inference product focused on small models. It’s useful for spiky workloads on 20+ task specific workloads across Search, OCR and Agentic execution.
Sign up for a $500 credits launch grant 👇
It’s powered by our ngine that has received thousands of github stars in the last few months, battle-tested across finance, legal & tech. We believe that self-hosting is going to be critical given all the trust & security issues in AI, but practically there are always workloads where you’d like somebody else to deal with the GPU quota like synthetic data generation, evals, public research and beyond.
The team at Superlinked has been burning the midnight oil for months and I’m so proud of every single one of you - what a privilege to call you my team mates y’all!
We built it, now help us make some noise 🙌
A like, a comment or a repost would mean a lot! ❤️
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A shelf camera can see the gap. Can your software prove which product is missing?
The proof is harder than the detection. Run OCR over the whole photo and you get every label on the shelf, and no idea which one sits under the hole. Spatial relationships are the real problem in xample solves them with four geometric rules: detect the gap, find the labels that overlap it vertically, pick the aligned pair, crop and upsample before OCR. On a real supermarket photo it identifies the missing product ("Panadol Child, 5-12Yrs Elixir 100ml") with zero hardcoded product knowledge.
Pure geometry, which is exactly what makes the audit reviewable by someone who wasn't in the store: superlinked.com/blog/retail-sh…
The proof is harder than the detection. Run OCR over the whole photo and you get every label on the shelf, and no idea which one sits under the hole. Spatial relationships are the real problem in xample solves them with four geometric rules: detect the gap, find the labels that overlap it vertically, pick the aligned pair, crop and upsample before OCR. On a real supermarket photo it identifies the missing product ("Panadol Child, 5-12Yrs Elixir 100ml") with zero hardcoded product knowledge.
Pure geometry, which is exactly what makes the audit reviewable by someone who wasn't in the store: superlinked.com/blog/retail-sh…
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What's up NYC AI Founders!? If you're building own AI moat with your own fine-tuned models, here's a hackathon on Nov 7th you don't want to miss!
LOCALHOST NYC: Sovereign AI Hackathon
We want to bring together builders, researchers, and AI enthusiasts in NYC who are solving the hard challenges of owning the vertical AI stack including building proprietary task specific models in Legal, Finance, Healthcare, Cybersecurity, etc. and are fine-tuning and serving models they build!
Organized by Daniel Marzec, Brenner Spear and featuring @Superlinked, @cognee_ , Tinfoil, @darkbloomai , Variant, and more.
📍 The School, Brooklyn, NYC
💰 $10K in prizes + infrastructure credits
Let's build the future of open and sovereign AI together!
🔗 Register: luma.com/cognee-m0ow
LOCALHOST NYC: Sovereign AI Hackathon
We want to bring together builders, researchers, and AI enthusiasts in NYC who are solving the hard challenges of owning the vertical AI stack including building proprietary task specific models in Legal, Finance, Healthcare, Cybersecurity, etc. and are fine-tuning and serving models they build!
Organized by Daniel Marzec, Brenner Spear and featuring @Superlinked, @cognee_ , Tinfoil, @darkbloomai , Variant, and more.
📍 The School, Brooklyn, NYC
💰 $10K in prizes + infrastructure credits
Let's build the future of open and sovereign AI together!
🔗 Register: luma.com/cognee-m0ow
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