Hey everyone! I bring you my development journey on what I have discovered, accomplishments for this...

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Netflix has described the production lessons behind bringing LLM inference into its internal serving platform, including the challenges of supporting different model sizes, hardware requirements, and rapidly evolving inference engines. By Matt Foster

Don't fight their strength — take away their advantage. Don't fight the boiling water — remove the...

Hey HackerNews, I built this project over the last few weeks as a palette cleanser from a failed game launch. I wanted to learn a bit about AI/Neural-Networks and naively thought I could build a tiny maze-solving AI in a weekend with a 100% solve rate. Well - I couldn't, but I got pretty close. 14 Bytes total model size, and a 96.5% solve rate on unseen mazes. Trained across 46 phases experimenting with different ideas to improve the model (better performance, smaller size). Its quite fun to wat

Most teams building AI agents treat manual override queues as a last resort, a safety net for when the agent goes off the rails. That view keeps agents locked in read-only mode on anything risky. The real insight is the opposite: a well-designed AI agent manual override queue enables you to trust your agent with high-value actions you would otherwise never automate. An override queue is not a punishment for poorly trained models. It is a design tool that widens the feasible autonomy boundary by giving you a structured escape hatch for the 5% of cases the agent cannot confidently handle. Table of Contents What Is an AI Agent Manual Override Queue? What Makes a Manual Override Queue Different from Other Queue Types The Lifecycle of an Action in a Manual Override Queue Four Steps to Implementing a Manual Override Queue in Your Agent Workflow How to Evaluate a Manual Override Queue Solution: Key Dimensions Three Pitfalls That Sabotage Manual Override Queues When to Use a Manual Override Queue, and When to Skip It Why We Built AwaitHuman: Escalation-as-a-Service for Agentic Workflows Frequently Asked Questions What Is an AI Agent Manual Override Queue? An AI agent manual override queue is a structured holding area where an autonomous agent's action is paused and routed to a human operator for review, approval, or rejection before execution. This is the formal definition from our guide to safe autonomous workflows (internal page, but we'll keep the reference general). The queue sits between the agent's decision and its execution, intercepting only the actions that cross a configurable risk threshold. For example, an agent managing cloud deployments might trigger a terraform apply command. Without a queue, that action happens immediately. With one, the agent serialises the full reasoning trace, the proposed diff, and the affected resources into a queue item. A human operator receives a notification, reviews the packet, and approves or rejects. The agent then proceeds or ba

Sequential Testing and the SPRT: How to Stop a Test Early Without Cheating Meta description: Peeking at a fixed-sample A/B test inflates false positives. Sequential testing lets you check results repeatedly and stop early without cheating. TL;DR Fixed-sample testing assumes you'll wait for a pre-calculated sample size before looking at results. Checking early and stopping the moment you see significance — "peeking" — quietly inflates your real false-positive rate, often far above the 5% you think you're getting. Abraham Wald's Sequential Probability Ratio Test (SPRT), developed for wartime quality control, is the mathematically rigorous alternative: a procedure built to be checked repeatedly, with pre-calculated boundaries that keep the false-positive rate honest by construction. The difference between the SPRT and peeking isn't willpower — it's that the SPRT's stopping rule is part of the math from the start, so stopping early doesn't cost you anything in error-rate control. Sequential design is the right call when traffic is limited, the cost of running a test too long is high, or the business genuinely can't commit to waiting for a fixed horizon — not a substitute for rigor, but a different kind of rigor suited to a different constraint. This is a methodology choice, not a shortcut — and it's one input into the broader question of how much certainty a given bet needs, covered in the Confidence Tier Model . Every experimentation program eventually hits the same moment: a test has been live for four days, the dashboard shows a lift, and someone — a stakeholder, a PM, sometimes you — asks "can we call it?" The honest answer depends entirely on what kind of test you designed, and most teams don't have a clean answer, because most teams designed a fixed-sample test and are now trying to read it like a sequential one. Those are not interchangeable. Knowing the difference, and choosing deliberately between them before the test starts, is the actual skill — not "wait lon

The Confidence Tier Model: How to Decide When Your Data Isn't Enough Meta description: Most testing programs are built for traffic they don't have. Three confidence tiers — proven, directional, speculative — each with its own bet-sizing rule. TL;DR Fixed-sample A/B testing assumes you can wait for statistical significance. Most teams can't — traffic is too thin, or the market is moving too fast to wait. The fix isn't lowering your standards. It's replacing the binary "significant / not significant" gate with three explicit confidence tiers — Proven, Directional, Speculative — each with its own evidence bar and its own bet-sizing rule. Underpowered tests systematically overestimate effect size (the "winner's curse" ). A confidence tier that accounts for this is more honest than a p-value that pretends otherwise. The way to move a learning up a tier isn't more of the same test — it's triangulation: stacking correlated, individually-weak signals until they converge. This is a methodology choice, not a compromise. Teams that name their confidence tier explicitly make faster, more defensible decisions than teams that either wait for certainty they'll never reach, or ship everything with false confidence. A product manager says: "Users want better deals." A brand marketer says: "TV is driving more direct demand." A performance marketer says: "This channel has a strong ROAS." Finance says: "But is this incremental?" Product says: "Will this hurt user trust?" Leadership says: "Should we scale this?" Six people, six kinds of evidence, and a decision that needs to get made this quarter — not whenever a test finally clears p<0.05. This is the actual job: not running tests, but converting six competing claims into one evidence base leadership can act on. Most experimentation methodology is written for a world where you have the traffic to wait for a clean answer. Most companies don't live in that world. The problem classic A/B testing doesn't solve Fixed-sample significance tes

📺 Prefer to watch? 90-second YouTube Short · 💬 Telegram Originally published on software-engineer-blog.com . You already know the trick: add "think step by step" to your prompt and the model's answer gets better. Almost nobody explains why — and the real reason has nothing to do with motivation or effort. Mental model: A transformer spends a fixed stack of layers per token, so adding reasoning tokens doesn't make the model smarter — it buys it more compute passes and an external scratchpad to read from. The Problem: Fixed Compute per Token Here's the floor. When a transformer generates a token, it runs through the same neural network layers every time. The stack depth is fixed at model-creation time. Whether you ask it "2+2" or "Sara has 3 packs of 8 markers, gives 5 to each of 4 friends, how many left?", the model gets the same amount of layered computation to produce each output token. That compute budget never grows with problem difficulty. Now imagine you ask for just the answer: "Sara has 3 packs of 8 markers, gives 5 to each of 4 friends, how many left? Answer only the number." The model has to solve a three-step problem (multiply 3 × 8 = 24, multiply 4 × 5 = 20, subtract 24 − 20 = 4) in a single forward pass. It needs to hold "24" and "20" somewhere while computing the final step. But it's only got one forward pass, one set of layer outputs, and nowhere internal to stash intermediate values. So it guesses. It might say 19. It didn't get the math wrong because it's bad at math. It got it wrong because you handed it the wrong compute budget for the job. The Mechanism: Three Small Shifts Now ask the same question and let it write the steps: "Sara has 3 packs of 8 markers, gives 5 to each of 4 friends, how many left? Think step by step." Three mechanical things happen: 1. The model becomes a loop. Every token the model emits is appended to the input context and fed back in on the next forward pass. So if it writes "First, 3 × 8 = 24", that token sequence gets rea

Regression and regularization are both important concepts in machine learning and statistics, but they solve different problems. Regression is primarily used to model relationships and make predictions. Regularization is used to improve a model's ability to generalize by controlling its complexity. Regression This is a statistical and machine learning technique used to predict a continuous numerical outcome based on one or more input variables. For example, we might want to predict: A house's price based on its size and location A student's exam score based on study hours A company's sales based on advertising spending Simple Linear Regression In simple linear regression, we model the relationship between an input variable (x) and an output (y): $$ y = \beta_0 + \beta_1x + \epsilon $$ Where: (y) is the predicted outcome (\beta_0) is the intercept (\beta_1) is the coefficient or slope (x) is the input variable (\epsilon) represents the error The model learns values for (\beta_0) and (\beta_1) that make its predictions as close as possible to the actual values. Multiple Linear Regression In multiple linear regression, several predictors are used: $$ y = \beta_0 + \beta_1x_1 + \beta_2x_2 + \cdots + \beta_px_p + \epsilon $$ The goal is typically to minimize the sum of squared errors (SSE) : $$ \text{SSE} = \sum_{i=1}^{n}(y_i - \hat{y}_i)^2 $$ This approach is known as Ordinary Least Squares (OLS) . Regularization Regularization is a technique used to prevent a machine learning model from becoming too complex. A model can perform extremely well on training data but poorly on new, unseen data. This problem is called overfitting . Regularization addresses overfitting by adding a penalty for large model coefficients to the model's objective function. Instead of minimizing only the prediction error, the model minimizes: $$ \text{Prediction Error} + \text{Complexity Penalty} $$ The penalty discourages the model from relying too heavily on individual features. The Main Types o

No pago $20/mes por Copilot. No tengo un Mac. Uso Linux Mint con un agente open-source, una API que cuesta céntimos y una terminal TUI. Este es mi setup real, lo bueno y lo que no te cuentan. No es postureo. Es pragmatismo. Mira, te voy a ser sincero. Cuando empecé a usar AI para programar probé de todo: Copilot, Cursor, Codeium, Continue... y cada uno tenía algo que no me cerraba. O era caro, o me ataba a un editor, o mandaba mi código a un servidor que no controlaba. Así que hice lo que haría cualquier developer cabezón: monté mi propio stack. Llevo unos meses con esta configuración y —spoiler— no he vuelto a abrir VSCode. Aquí te cuento qué uso, por qué, y lo que me costó que funcionara bien. El stack (sin humo) Linux Mint 22 ← SO base (porque funciona y no da guerra) └─ Hermes Agent TUI ← Agente open-source de Nous Research ├─ DeepSeek V4 Pro API ← El cerebro (1M contexto, $0.43/M tokens) ├─ Gemini API (free) ← Búsquedas y consultas web └─ Terminal (Alacritty) ← Donde vivo el 90% del tiempo Tres piezas. Sin IDEs de pago. Sin lock-in. Sin depender de que OpenAI no suba precios otra vez. ¿Por qué DeepSeek y no Claude/GPT? He probado los tres. Mi razonamiento: DeepSeek V4 Pro Claude Sonnet GPT-4o Precio (1M tokens in) $0.43 $3.00 $2.50 Ventana de contexto 1M tokens 200K 128K Calidad de código ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ Razonamiento largo ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ DeepSeek me da el 85-90% de la calidad de Claude por el 15% del precio. Para el 90% de las tareas diarias —refactorizar, debuggear, generar boilerplate, explicar código— la diferencia no se nota. Cuando necesito razonamiento ultra-profundo para algo muy complejo, cambio a Claude. Pero son momentos puntuales. El día a día es DeepSeek. 💡 El dato que nadie dice: Con DeepSeek V4 Pro gasto ~$5-8 al MES programando 4-6 horas al día. Con Claude o GPT estaría en $40-60. La diferencia paga mi VPS. Hermes Agent: el agente que no sabías que necesitabas Hermes Agent es un agente open-source (MIT) de Nous Research. 219K estrellas en GitHub

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