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Same model. Up to 65% less. DeepSeek’s new API prices take effect on August 16. Marathon lets latency-tolerant workloads trade wait time for lower inference costs without switching models. ▷ Choose NOW when every second matters. ▷ Choose SOON or LATER when a few minutes are acceptable. ▷ Choose...

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Marathon

2,688 subscribers

15,113 Aufrufe • vor 1 Monat •via X (Twitter)

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2 Kommentare

Profilbild von AI BIRD
AI BIRDvor 1 Monat

Hi Marathon Team, I’ve been using Marathon Delayed Inference and found that tasks are constantly stuck in queued state. How should I fix this? I can supply more details if required, thanks.

Profilbild von BlockWeb
BlockWebvor 1 Monat

The ability to trade latency for cost is a pretty smart model.

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49,101 Aufrufe • vor 10 Tagen

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You go on vacation and rent an Airbnb, only to show up and find more rules than a jail. Here are just a few: ➡️The owner left their food in the refrigerator, so they left a "rule" not to touch their food. ➡️Remove shoes when you come into the house ➡️Squeege the shower after each use, even though you pay for cleaning. ➡️Do not move the showerhead. ➡️Do not wash the bedding. There are literally rules posted in every room of the house, some with multiple rules. Is this over-the-top or acceptable?

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33,240 Aufrufe • vor 5 Monaten

Every voice matters, and every stakeholder will be at the table—because Philadelphians deserve nothing less. Not everyone has the luxury of a car or the option to call an Uber. For many, reliable transit is the difference between getting to work, keeping medical appointments, or making sure our kids get to school on time. I believe—wholeheartedly—that we can reach a resolution. But it’s going to take everyone, across party lines, working together with urgency and commitment. We are not giving up. But I urge every Philadelphian: please make a plan now so you and your family are prepared if the time comes. Because at the end of the day, this isn’t just politics—this is about people. And I will never stop fighting for you.
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Every voice matters, and every stakeholder will be at the table—because Philadelphians deserve nothing less. Not everyone has the luxury of a car or the option to call an Uber. For many, reliable transit is the difference between getting to work, keeping medical appointments, or making sure our kids get to school on time. I believe—wholeheartedly—that we can reach a resolution. But it’s going to take everyone, across party lines, working together with urgency and commitment. We are not giving up. But I urge every Philadelphian: please make a plan now so you and your family are prepared if the time comes. Because at the end of the day, this isn’t just politics—this is about people. And I will never stop fighting for you.

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I’ve been on TikTok the whole night going through the conversations, and honestly, Babu badly miscalculated with that press conference. The backlash is loud, and the support doesn’t look anything close to what he may have imagined. A handful of people around him can keep cheering, but that doesn’t mean the wider public is with you. Leadership is not about responding to every criticism, every blogger or every noise online. Sometimes the smartest thing a leader can do is shut up, think, and choose his battles. You don’t engage your mouth before you engage your brain. Ego can make you speak today and regret it tomorrow. If you want to lead, learn when to talk, when to listen and when to keep quiet.
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I’ve been on TikTok the whole night going through the conversations, and honestly, Babu badly miscalculated with that press conference. The backlash is loud, and the support doesn’t look anything close to what he may have imagined. A handful of people around him can keep cheering, but that doesn’t mean the wider public is with you. Leadership is not about responding to every criticism, every blogger or every noise online. Sometimes the smartest thing a leader can do is shut up, think, and choose his battles. You don’t engage your mouth before you engage your brain. Ego can make you speak today and regret it tomorrow. If you want to lead, learn when to talk, when to listen and when to keep quiet.

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YOU ARE NOT JUST A REAL ARTIST IF YOU USE AI. YOU ARE NOT JUST A REAL MUSICIAN. YOU ARE NOT JUST A REAL WRITER. You are still the one making the choices. What stays. What goes. What gets distorted. What gets rebuilt. What deserves another hour. What needs to be thrown away and started again. The tools can change. Taste still matters. Intent still matters. Imagination still matters. And when you choose to work with tools before everyone has agreed they are legitimate, some people will dismiss the work before they even look at it. Let them. Every new creative language looks wrong to someone before enough people learn how to speak it. Keep pushing. The best is yet to come. MORE THAN REAL | A surRIHL Cut Created with LoLo.

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15,242 Aufrufe • vor 9 Monaten

46 million working Americans don’t have a 401k plan, pension plan or other savings plan for their retirement. Tonight, President Donald J. Trump announced that he was going to create a savings plan that will model that of the Thrift Savings Plan for government employees. Let’s call it the Trump Savings Plan. The Trump Savings Plan will allow workers without a savings plan to put aside a portion of their income every year with a Federal match of $1,000 — with the potential for the match to increase with congressional approval — that will be invested in the stock market and will grow tax free until their retirement. We need every working American to own a piece of capitalism so that while they sleep, they can save for their retirement and the next generation. And importantly, the program pays for itself even with a 5% government match, as the participants retire with the resources they need, and are no longer reliant on welfare programs and are not a future burden to the welfare system. I predict that increasing the government match for the Trump Savings Plan will be one of the few bipartisan pieces of legislation approved by Congress next year for how can a member of Congress vote against giving every working American the same savings plan that they have? This is an amazing initiative for our country. Thank you President Trump for your leadership!
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46 million working Americans don’t have a 401k plan, pension plan or other savings plan for their retirement. Tonight, President Donald J. Trump announced that he was going to create a savings plan that will model that of the Thrift Savings Plan for government employees. Let’s call it the Trump Savings Plan. The Trump Savings Plan will allow workers without a savings plan to put aside a portion of their income every year with a Federal match of $1,000 — with the potential for the match to increase with congressional approval — that will be invested in the stock market and will grow tax free until their retirement. We need every working American to own a piece of capitalism so that while they sleep, they can save for their retirement and the next generation. And importantly, the program pays for itself even with a 5% government match, as the participants retire with the resources they need, and are no longer reliant on welfare programs and are not a future burden to the welfare system. I predict that increasing the government match for the Trump Savings Plan will be one of the few bipartisan pieces of legislation approved by Congress next year for how can a member of Congress vote against giving every working American the same savings plan that they have? This is an amazing initiative for our country. Thank you President Trump for your leadership!

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your agent has thirty tools. it calls two of them. the other twenty eight are not sitting idle somewhere. they are in the request, every request, and they are doing damage in two places at once. first the obvious one. tool schemas go into the prompt, and a schema is not a name. it is a description, a parameter list, types, required fields, an example. thirty of those is a few thousand tokens that ship with every single call, including the ones where the agent just says thanks and stops. you are paying rent on twenty eight tools that have never fired. second, and this is the one that costs more. when the request says cancel the order, the model picks by matching against everything available. four of your tools are plausible: cancel_order, refund_order, update_order, void_order. it is choosing among them based on the descriptions you wrote, one afternoon, months ago. every tool you add is another candidate in that shortlist. the twenty eight you never call are not neutral. they are noise in the one decision that determines whether the run works. > why it grows without anyone deciding to nobody adds thirty tools on purpose. you add one for a task, it works, it stays. six months later the registry is a catalogue and no one has ever removed anything, because removing a tool feels risky and adding one feels free. and there is no feedback telling you otherwise. the unused ones never error. they never appear in a failing trace. they are invisible in exactly the way that lets them accumulate. > what to actually do count calls per tool over the last thousand runs. this is one group-by and it usually shocks people. the ones at zero are pure cost. ship the tools the task needs, not the whole registry. a research phase does not need deploy. a writing phase does not need the database. swap the set between phases instead of loading everything up front. same agent, different tools, depending on where the run is. and when two tools could both plausibly answer the same request, that is not redundancy you can ignore. it is a coin flip you built into the system. the twenty eight tools are not unused. they are used every time, by the part of the run you cannot see.
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your agent has thirty tools. it calls two of them. the other twenty eight are not sitting idle somewhere. they are in the request, every request, and they are doing damage in two places at once. first the obvious one. tool schemas go into the prompt, and a schema is not a name. it is a description, a parameter list, types, required fields, an example. thirty of those is a few thousand tokens that ship with every single call, including the ones where the agent just says thanks and stops. you are paying rent on twenty eight tools that have never fired. second, and this is the one that costs more. when the request says cancel the order, the model picks by matching against everything available. four of your tools are plausible: cancel_order, refund_order, update_order, void_order. it is choosing among them based on the descriptions you wrote, one afternoon, months ago. every tool you add is another candidate in that shortlist. the twenty eight you never call are not neutral. they are noise in the one decision that determines whether the run works. > why it grows without anyone deciding to nobody adds thirty tools on purpose. you add one for a task, it works, it stays. six months later the registry is a catalogue and no one has ever removed anything, because removing a tool feels risky and adding one feels free. and there is no feedback telling you otherwise. the unused ones never error. they never appear in a failing trace. they are invisible in exactly the way that lets them accumulate. > what to actually do count calls per tool over the last thousand runs. this is one group-by and it usually shocks people. the ones at zero are pure cost. ship the tools the task needs, not the whole registry. a research phase does not need deploy. a writing phase does not need the database. swap the set between phases instead of loading everything up front. same agent, different tools, depending on where the run is. and when two tools could both plausibly answer the same request, that is not redundancy you can ignore. it is a coin flip you built into the system. the twenty eight tools are not unused. they are used every time, by the part of the run you cannot see.

Hanako

24,656 Aufrufe • vor 1 Monat

If you plan on being caretakers of your parents or siblings, have them put their home and assets in a trust. They will qualify for Medicaid services after five years of showing minimal income. Medicaid would then in turn pay you to care for them. Now Caretaking is an overwhelming task and many are not built for it so if that's you still have them put the home in a trust and the assets to qualify for Medicaid...they will pay for whomever you choose to care for them. For end of life care..your parents should own nothing and have no assets that are not in a protected trust.
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If you plan on being caretakers of your parents or siblings, have them put their home and assets in a trust. They will qualify for Medicaid services after five years of showing minimal income. Medicaid would then in turn pay you to care for them. Now Caretaking is an overwhelming task and many are not built for it so if that's you still have them put the home in a trust and the assets to qualify for Medicaid...they will pay for whomever you choose to care for them. For end of life care..your parents should own nothing and have no assets that are not in a protected trust.

!Nspirenaire〽️

347,938 Aufrufe • vor 10 Monaten

THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j*,j*) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j*,j*) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window
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THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j*,j*) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j*,j*) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window

Yarchi

22,966 Aufrufe • vor 4 Monaten

Production traffic is not uniform. You get a few requests that need your best model, but most are simple questions and lookups you can solve with cheaper, faster models. The most expensive mistake you can make today is sending every request to your strongest model. You need routing. Period. This is the simplest trick to improve the architecture of whatever you are building. Please, don't implement routing yourself. You don't have to. I'm currently working with TrueFoundry's Auto Routing. It reads each request, classifies it as simple, medium, or complex, and sends it to the model assigned to that tier. You have two choices: 1. Send every request to the free heuristic classifier to score signals such as technical vocabulary, code, prompt length, and multi-step reasoning. 2. Send the request to an LLM classifier when its difficulty requires a more nuanced judgment. The beauty of using routing is that nothing changes in your code. You still call a single endpoint model, but routing works behind the scenes to pair every request with the best possible model. TrueFoundry ran several experiments with two different setups: 1. Send every request to Claude Opus 2. Send every request to a router with Haiku, Sonnet, and Opus The first experiment ran 550 deterministically graded academic prompts through every setup. Auto Routing was 69% cheaper while retaining 98% of the baseline quality. The second experiment ran three production-shaped workloads through every setup, using user chats, developer chats, and long agent tasks. Auto Routing was 80% cheaper. Thanks to the TrueFoundry team for partnering with me on this post.
1:28

Production traffic is not uniform. You get a few requests that need your best model, but most are simple questions and lookups you can solve with cheaper, faster models. The most expensive mistake you can make today is sending every request to your strongest model. You need routing. Period. This is the simplest trick to improve the architecture of whatever you are building. Please, don't implement routing yourself. You don't have to. I'm currently working with TrueFoundry's Auto Routing. It reads each request, classifies it as simple, medium, or complex, and sends it to the model assigned to that tier. You have two choices: 1. Send every request to the free heuristic classifier to score signals such as technical vocabulary, code, prompt length, and multi-step reasoning. 2. Send the request to an LLM classifier when its difficulty requires a more nuanced judgment. The beauty of using routing is that nothing changes in your code. You still call a single endpoint model, but routing works behind the scenes to pair every request with the best possible model. TrueFoundry ran several experiments with two different setups: 1. Send every request to Claude Opus 2. Send every request to a router with Haiku, Sonnet, and Opus The first experiment ran 550 deterministically graded academic prompts through every setup. Auto Routing was 69% cheaper while retaining 98% of the baseline quality. The second experiment ran three production-shaped workloads through every setup, using user chats, developer chats, and long agent tasks. Auto Routing was 80% cheaper. Thanks to the TrueFoundry team for partnering with me on this post.

Santiago

15,543 Aufrufe • vor 16 Tagen

Chinese researchers just published a paper with a brutal title: "The End of Model Loyalty". The thing is f*cking gold, and the argument is simple: staying on one flagship is a tax, not a preference. Two frontier models now share the same million-token window. One rents at $10 in and $50 out, one downloads at $3 in and $15 out with open weights. On the median task their benchmarks overlap. On the hard tail one completes 99.9% against 62.7% -- and that gap is the only thing worth paying up for. So their answer is a router, not a favorite: price every job before dispatch, send the bulk to the open width, reserve the ceiling for finalists. Their production run split 84% width, 16% ceiling -- $256 for a workload the flagship alone prices at $4,318. Loyalty is a subscription. Routing is a strategy. Which model takes which job, dollar by dollar, is in the article ↓
0:30

Chinese researchers just published a paper with a brutal title: "The End of Model Loyalty". The thing is f*cking gold, and the argument is simple: staying on one flagship is a tax, not a preference. Two frontier models now share the same million-token window. One rents at $10 in and $50 out, one downloads at $3 in and $15 out with open weights. On the median task their benchmarks overlap. On the hard tail one completes 99.9% against 62.7% -- and that gap is the only thing worth paying up for. So their answer is a router, not a favorite: price every job before dispatch, send the bulk to the open width, reserve the ceiling for finalists. Their production run split 84% width, 16% ceiling -- $256 for a workload the flagship alone prices at $4,318. Loyalty is a subscription. Routing is a strategy. Which model takes which job, dollar by dollar, is in the article ↓

slash1s

13,518 Aufrufe • vor 13 Tagen