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Can you solve these agentic AI math problems by hand ✍️? Getting a bit harder now. Download PDF: Problems 11 to 15: 11. Comparing three calls: same total tokens, three different bills 12. The average call: size a sample, then scale it to the day 13. The system prompt:...

41,898 просмотров • 2 месяцев назад •via X (Twitter)

Комментарии: 5

Фото профиля Harley Lewis Foote
Harley Lewis Foote2 месяцев назад

problem 11 is the whole agent economy in one question: same tokens, three different bills

Фото профиля Polash Khan
Polash Khan2 месяцев назад

This looks like a fun challenge! Let me see if I can tackle these.

Фото профиля Harley Lewis Foote
Harley Lewis Foote2 месяцев назад

These problems sit right on the frontier's teeth. Combinatorial reasoning with token-count constraints is where capability drops off non-monotonically. The jaggedness repeats even inside math, so 'harder now' might mean 'different failure mode entirely'.

Фото профиля Renat Lotfullin
Renat Lotfullin2 месяцев назад

Any chance graph model by hand in future?

Фото профиля Waleed Rafi Abbasi
Waleed Rafi Abbasi2 месяцев назад

Based on profit or loss

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Redis built a cache that cuts LLM costs by 90%! Production LLM apps do not receive completely new questions every time. A customer-support assistant might receive all three of these: - "Can I get a refund after buying the monthly plan?" - "Is the monthly subscription refundable?" - "Can I cancel the plan and get my money back?" The wording is different, but the underlying question and its answer remain the same. Yet LLM apps process every version as a new request. They assemble the prompt, send it to the model, and generate an answer that may have already been generated. Prefix caching reduces part of these repeated calls. When requests begin with the same system prompt or context, the model can reuse the KV states already computed for that shared prefix. But the request still hits the LLM. The new tokens must be processed, and the complete answer must still be decoded. So even with a prefix-cache hit, there's another generation call involved. To solve this, instead of only caching computation inside the model, the application can cache the generated response outside it. When another question arrives, the system embeds it and compares it with previously answered questions. If it finds a sufficiently close match, it returns the stored response without invoking the LLM again. A cache hit removes the input tokens, output tokens, and decoding time associated with another LLM call. In practice, it is important to decide which questions can safely share an answer since a production setup needs well-tuned similarity thresholds, expiration policies, data isolation, and monitoring for incorrect matches. If you want to use this in practice, Redis already implements it as a managed service called Redis LangCache. Under the hood, it generates embeddings, searches previous responses, and returns a matching answer before another model call occurs. Redis also handles access scopes, custom filtering, TTL and eviction controls, and cache monitoring through Redis Cloud. I built an interface to compare it against direct LLM inference. The video below shows this in action, and I worked with Redis on this post to put this together. For the paraphrased question in my run, direct inference took 2.232 seconds and consumed 514 input tokens plus 250 output tokens. Redis returned the earlier response in 0.37 seconds with zero LLM input or output tokens. That was roughly 6x faster in this run. Redis reports API cost savings of up to 90% and cache-hit responses up to 15x faster. The actual result depends on how much safe repetition exists in the workload. You can try Redis LangCache here: If you want to dive deeper, I have already written a detailed breakdown of KV, prefix, prompt, and semantic caching in the article quoted below. This demo builds on the final technique and shows it running in practice. Read it below.

Avi Chawla

166,230 просмотров • 1 месяц назад

Harness vs. Graphs, clearly explained! a harness is great, and most people think it is the whole thing: retries, timeouts, a sandbox, a log, the context it assembles before every call. all of that is real work, and all of it wraps exactly one call. run it a hundred times and you have one call, made very safely, a hundred times. Graph engineering fixes this by moving the decision up a layer: not how safely one call is made, but which calls exist to be made at all. you need both, and here is the sentence that resolves the whole confusion: the harness is everything around one call. the graph is everything between them. ↳ around one call: retry, timeout, sandbox, log, assemble the context, hand back a result ↳ between calls: split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs the harness does not go away when you build a graph. it moves under each node, and now there are five of them, each wrapping a call you would never have made by hand. the trick is knowing which layer a failure belongs to. turn a piece off and run it again. if the call still works, it was the harness. if the wrong step runs at all, it was the graph. people spend weeks hardening a harness around a node that should not have existed. one thing to know before you scale it. most of what people call their agent is a harness with a chat box on it. ↳ it retries, it times out, it logs, it assembles context, it holds one call up beautifully ↳ it has never once decided that a second call should exist, and that is the entire difference that last one catches careful people. a harness that never fails is not evidence the system is right. it is evidence one call went well, which is the smallest possible claim. and the one that eats whole nights: a harness cannot save you from the wrong step running. you can retry a bad decision three times with a clean log and perfect isolation, and all you bought was three copies of it. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Hanako

51,717 просмотров • 1 месяц назад

A 19 year old gets on Zoom calls with business owners and shows them how they're about to throw away $600,000. He opens with one question. How much are you paying your receptionist? The dental office owner says $60,000. The kid says: over the next 10 years that's $600,000 going to someone who calls in sick, takes lunch breaks and goes home at 5. For a job a machine can do for $12,000 once. Then the second question. How many calls do you miss every day? Voicemails nobody calls back. People who hang up after three rings. New patients who try once and never try again. The owner stops. Pulls up his phone log. Counts. 8 missed calls a day. Sometimes more. Each one is a $200 patient walking past the front desk while it's empty for lunch. $1,600 a day. Almost $600,000 a year in revenue he never knew was leaving. That number sits on top of the $600,000 in salary. The owner is staring at a chart he never let himself draw before. That's when the kid says it. I can build you something for $12,000. Picks up every call. Doesn't sleep. Doesn't take vacation. Books patients while you're operating on someone else. Pays for itself in the first week. The $12,000 invoice doesn't feel like an expense. It feels like a refund. The kid closes 3 to 4 of these calls a month. $50,000 in revenue. He's 19. Behind the scenes he's not building anything from scratch. He's running Kimi K2.6 with 300 parallel sub agents, paying $0.60 per million tokens instead of $5 with Claude. The same reception agent a real developer would charge $40,000 to build, he ships in 30 minutes. 12 hour autonomous sessions. Zero human involvement. One prompt and the agent goes live. Answering calls. Booking patients. Routing emergencies. Before the owner finishes his coffee. His friends are working summer shifts at coffee shops for $14 an hour. He's making $600,000 a year by walking into a Zoom call and naming the most uncomfortable number a business owner has on his books. His secret isn't technology. He never argues with the owner about whether AI works. He hands the owner a calculator and asks the questions the owner has been avoiding for years. By minute 8 the owner has done the math himself. By minute 12 the kid quotes the price. By minute 15 the contract is signed. He told his dad about it once. His dad is an insurance salesman. 30 years selling the same product to every client for the same reason: fear of losing what they already have. His dad listened. Then said: you sell the same thing I sell. You just call it AI. The kid said: I don't even call it that. I let them call it whatever they want. His dad smiled. Walked out of the room. The kid had another call in 10 minutes.

Marlow

295,813 просмотров • 5 месяцев назад