# ByteByteGo

Explain complex systems with simple terms, from the authors of the best-selling system design book series. Join over 1,000,000 friendly readers.

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## How LLMs Can Find a Needle in a Haystack

DevFeed: [How LLMs Can Find a Needle in a Haystack](<https://devfeed.tech/articles/how-llms-can-find-a-needle-in-a-haystack-31466.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/how-llms-can-find-a-needle-in-a-haystack>)

Author: ByteByteGo

Published: 2026-09-16T15:31:42Z

Content type: tutorial

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [App](<https://devfeed.tech/topics/app.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-applications](<https://devfeed.tech/tags/llm-applications.md>), [llms](<https://devfeed.tech/tags/llms.md>)

### AI overview

This tutorial explains the retrieval problem in LLM applications: finding current, relevant evidence in large collections of private documents. It discusses embeddings, semantic matching, search scope, and changing answers.

### Source excerpt

In this article, we are going to look at how LLMs can find a needle in a haystack.

## Build with Claude Code: Enrollment for a Two-Day Cohort Course

DevFeed: [Build with Claude Code: Enrollment for a Two-Day Cohort Course](<https://devfeed.tech/articles/last-call-for-enrollment-build-with-claude-code-26893.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/last-call-for-enrollment-build-with-d63>)

Author: ByteByteGo

Published: 2026-09-15T19:31:01Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Claude Code](<https://devfeed.tech/topics/claude-code.md>), [Development](<https://devfeed.tech/topics/development.md>), [Code](<https://devfeed.tech/topics/code.md>), [Git](<https://devfeed.tech/topics/git.md>)

Tags: [build](<https://devfeed.tech/tags/build.md>), [claude-code](<https://devfeed.tech/tags/claude-code.md>), [code](<https://devfeed.tech/tags/code.md>), [development](<https://devfeed.tech/tags/development.md>), [git](<https://devfeed.tech/tags/git.md>), [hooks](<https://devfeed.tech/tags/hooks.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [learn](<https://devfeed.tech/tags/learn.md>), [mcps](<https://devfeed.tech/tags/mcps.md>), [parallel](<https://devfeed.tech/tags/parallel.md>)

### AI overview

The article announces the relaunch of Build with Claude Code, a two-day cohort-based course taught by John Kim. It covers Claude Code fundamentals and production workflows, including context engineering, memory, Skills, MCPs, hooks, Git worktrees, subagents, and agent teams.

### Source excerpt

We're relaunching Build with Claude Code, a 2-day intensive cohort-based course taught by John Kim, who has trained hundreds of engineers at Meta to use Claude Code in real production workflows.

## How LLMs Handle Memory Through Context and Surrounding Applications

DevFeed: [How LLMs Handle Memory Through Context and Surrounding Applications](<https://devfeed.tech/articles/do-llms-have-the-memory-of-a-goldfish-26892.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/do-llms-have-the-memory-of-a-goldfish>)

Author: ByteByteGo

Published: 2026-09-15T15:31:12Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [context window](<https://devfeed.tech/topics/context-window.md>), [App](<https://devfeed.tech/topics/app.md>), [long-context](<https://devfeed.tech/topics/long-context.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [context](<https://devfeed.tech/tags/context.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [cost](<https://devfeed.tech/tags/cost.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llms](<https://devfeed.tech/tags/llms.md>), [memory](<https://devfeed.tech/tags/memory.md>)

### AI overview

LLMs do not usually retain personal or persistent memory between interactions. Surrounding applications create the appearance of memory by storing messages, maintaining summaries, retrieving relevant information, and supplying it to the model. As conversations grow, this processing increases cost and latency, while context-window limits require older information to be removed, summarized, or stored elsewhere.

### Source excerpt

In this article, we will learn how LLMs handle memory so that they are useful to end users in performing complex tasks that require conversation and holding context.

## LLMs as a Judge: How to Know if Your LLM is Healthy

DevFeed: [LLMs as a Judge: How to Know if Your LLM is Healthy](<https://devfeed.tech/articles/llms-as-a-judge-how-to-know-if-your-llm-is-healthy-17997.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/llms-as-a-judge-how-to-know-if-your>)

Author: ByteByteGo

Published: 2026-09-14T15:31:02Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [Testing](<https://devfeed.tech/topics/testing.md>), [LLMs](<https://devfeed.tech/topics/llms.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [llms](<https://devfeed.tech/tags/llms.md>), [testing](<https://devfeed.tech/tags/testing.md>)

### AI overview

This article explains how to evaluate large language model applications, including why conventional software tests are insufficient, how LLM-as-a-Judge works, and how to combine automated checks, curated datasets, human review, and production monitoring.

### Source excerpt

In this article, we are going to look at the process of LLM evaluation in detail.

## EP225: Why Does Git Revert Cause Conflicts?

DevFeed: [EP225: Why Does Git Revert Cause Conflicts?](<https://devfeed.tech/articles/ep225-why-does-git-revert-cause-conflicts-17987.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/ep225-why-does-git-revert-cause-conflicts>)

Author: ByteByteGo

Published: 2026-09-12T15:30:46Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Git](<https://devfeed.tech/topics/git.md>), [Claude Code](<https://devfeed.tech/topics/claude-code.md>)

Tags: [code](<https://devfeed.tech/tags/code.md>), [git](<https://devfeed.tech/tags/git.md>), [review](<https://devfeed.tech/tags/review.md>)

### AI overview

The article explains why git revert can cause conflicts when a later commit changes the same lines as the earlier commit being undone. It contrasts revert with reset and outlines how to resolve the conflict manually before continuing the revert.

### Source excerpt

git revert looks straightforward until it throws a conflict

## Learn Claude Code, evals, AI systems, and more: ByteByteGo Live is here

DevFeed: [Learn Claude Code, evals, AI systems, and more: ByteByteGo Live is here](<https://devfeed.tech/articles/learn-claude-code-evals-ai-systems-and-more-bytebytego-live-is-here-17996.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/learn-claude-code-evals-ai-systems>)

Author: ByteByteGo

Published: 2026-09-11T15:32:16Z

Content type: release

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Claude Code](<https://devfeed.tech/topics/claude-code.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [AI Engineering](<https://devfeed.tech/topics/ai-engineering.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-development](<https://devfeed.tech/tags/ai-development.md>), [ai-engineering](<https://devfeed.tech/tags/ai-engineering.md>), [ai-evals](<https://devfeed.tech/tags/ai-evals.md>), [claude-code](<https://devfeed.tech/tags/claude-code.md>), [development](<https://devfeed.tech/tags/development.md>)

### AI overview

ByteByteGo announces ByteByteGo Live, a membership offering live courses on Claude Code, production AI systems, AI engineering, AI evaluations, cost optimization, and related topics. The announcement cites higher completion rates for live cohorts and says the membership covers courses offered over the next 12 months.

### Source excerpt

Most online courses never get finished (~4% completion). Live cohorts get ~40%, roughly 10x higher. Live courses are the only courses people actually finish. So we're launching ByteByteGo Live.

## A Guide to Application Networking Basics

DevFeed: [A Guide to Application Networking Basics](<https://devfeed.tech/articles/a-guide-to-application-networking-basics-17982.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/a-guide-to-application-networking>)

Author: ByteByteGo

Published: 2026-09-10T15:31:28Z

Content type: tutorial

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [networking](<https://devfeed.tech/topics/networking.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [guide](<https://devfeed.tech/tags/guide.md>), [networking](<https://devfeed.tech/tags/networking.md>)

### AI overview

The article introduces application networking basics and states that it will examine various aspects of networking in detail. The supplied text does not provide further technical findings.

### Source excerpt

In this article, we will look at the various aspects of networking in detail.

## How Smart Model Routing Can Cut LLM Costs by 10X

DevFeed: [How Smart Model Routing Can Cut LLM Costs by 10X](<https://devfeed.tech/articles/how-smart-model-routing-can-cut-llm-costs-by-10x-17990.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/how-smart-model-routing-can-cut-llm>)

Author: ByteByteGo

Published: 2026-09-09T15:30:26Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Model Routing](<https://devfeed.tech/topics/model-routing.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [API](<https://devfeed.tech/topics/api.md>)

Tags: [cost](<https://devfeed.tech/tags/cost.md>), [llm](<https://devfeed.tech/tags/llm.md>), [model-routing](<https://devfeed.tech/tags/model-routing.md>), [models](<https://devfeed.tech/tags/models.md>), [routing](<https://devfeed.tech/tags/routing.md>), [routing-system](<https://devfeed.tech/tags/routing-system.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

This article explains how smart model routing can reduce LLM application costs by sending simple requests to less expensive models and difficult requests to more capable models. It notes that savings and response quality depend on request types, model price differences, and routing performance, and discusses router, cascading, semantic, and learned routing approaches.

### Source excerpt

Cost reduction isn't a given. It also depends on the types of requests the application receives, the price difference between models, and how well the routing system performs. In this article, we are going to look at various aspects

## Built for Reliability: How American Express Processes Payments at Scale

DevFeed: [Built for Reliability: How American Express Processes Payments at Scale](<https://devfeed.tech/articles/built-for-reliability-how-american-express-processes-payments-at-scale-17984.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/built-for-reliability-how-american>)

Author: ByteByteGo

Published: 2026-09-08T18:31:03Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [Microservice](<https://devfeed.tech/topics/microservice.md>), [Microservices](<https://devfeed.tech/topics/microservices.md>), [Network](<https://devfeed.tech/topics/network.md>), [Server](<https://devfeed.tech/topics/server.md>)

Tags: [account-balance](<https://devfeed.tech/tags/account-balance.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [bank](<https://devfeed.tech/tags/bank.md>), [card](<https://devfeed.tech/tags/card.md>), [cell-based-architecture](<https://devfeed.tech/tags/cell-based-architecture.md>), [microservices](<https://devfeed.tech/tags/microservices.md>), [network](<https://devfeed.tech/tags/network.md>), [payments](<https://devfeed.tech/tags/payments.md>), [scale](<https://devfeed.tech/tags/scale.md>), [server](<https://devfeed.tech/tags/server.md>), [services](<https://devfeed.tech/tags/services.md>)

### AI overview

The article explains how American Express processes payments through a cell-based architecture. Transactions move through independent processing units and microservices, with isolation designed to limit disruption when services fail.

### Source excerpt

In this article, we will try to understand how the transaction runs through such a cell-based architecture and how the payments are processed even when some services are failing.

## How to Deal With Errors and Failures in LLM-Powered Applications

DevFeed: [How to Deal With Errors and Failures in LLM-Powered Applications](<https://devfeed.tech/articles/how-to-deal-with-errors-and-failures-in-llm-powered-applications-17991.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/how-to-deal-with-errors-and-failures>)

Author: ByteByteGo

Published: 2026-09-07T15:31:35Z

Content type: tutorial

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [App](<https://devfeed.tech/topics/app.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [applications](<https://devfeed.tech/tags/applications.md>), [concurrency-control](<https://devfeed.tech/tags/concurrency-control.md>), [errors](<https://devfeed.tech/tags/errors.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [llm](<https://devfeed.tech/tags/llm.md>), [rate-limiting](<https://devfeed.tech/tags/rate-limiting.md>), [resiliency](<https://devfeed.tech/tags/resiliency.md>), [retry](<https://devfeed.tech/tags/retry.md>)

### AI overview

A tutorial on error handling and resiliency for LLM-powered applications. It covers failures such as unavailable networks, rejected requests, invalid JSON, hallucinations, slow responses, timeouts, retries, fallbacks, circuit breakers, rate limiting, queues, concurrency control, safe tool calls, and response streaming.

### Source excerpt

Apart from normal processing, the application also sends data to a large language model (LLM). It then uses the model's response to carry out a task.

## EP224: MCP vs RAG vs AI Agents

DevFeed: [EP224: MCP vs RAG vs AI Agents](<https://devfeed.tech/articles/ep224-mcp-vs-rag-vs-ai-agents-17986.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/ep224-mcp-vs-rag-vs-ai-agents>)

Author: ByteByteGo

Published: 2026-09-05T15:30:58Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Publish-subscribe pattern](<https://devfeed.tech/topics/pubsub.md>), [Replication](<https://devfeed.tech/topics/replication.md>)

Tags: [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [messaging](<https://devfeed.tech/tags/messaging.md>), [rag](<https://devfeed.tech/tags/rag.md>), [replication](<https://devfeed.tech/tags/replication.md>)

### AI overview

This article compares MCP, RAG, and AI agents. It explains MCP as a standard protocol for connecting AI models to external tools and data, RAG as a way to retrieve current information from external sources, and AI agents as systems that perform tasks and make decisions autonomously. It also summarizes several distributed-systems patterns, including replication, sharding, publish-subscribe, circuit breakers, retries, and leader election.

### Source excerpt

An AI agent is kind of an AI system where the agent performs the task autonomously and takes the decisions.

## How Databases Keep Their Sanity with Concurrency Control

DevFeed: [How Databases Keep Their Sanity with Concurrency Control](<https://devfeed.tech/articles/how-databases-keep-their-sanity-with-concurrency-control-17989.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/how-databases-keep-their-sanity-with>)

Author: ByteByteGo

Published: 2026-09-03T15:31:17Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Databases](<https://devfeed.tech/topics/databases.md>), [Concurrency](<https://devfeed.tech/topics/concurrency.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [concurrency-control](<https://devfeed.tech/tags/concurrency-control.md>), [databases](<https://devfeed.tech/tags/databases.md>)

### AI overview

The article introduces how databases handle bugs related to concurrency control, but the supplied evidence does not provide the specific methods or findings discussed.

### Source excerpt

So how do we handle such bugs? This is what we are going to try to answer in this article.

## How Embedding Models Affect Retrieval-Augmented Generation Quality

DevFeed: [How Embedding Models Affect Retrieval-Augmented Generation Quality](<https://devfeed.tech/articles/why-your-rag-system-is-only-as-good-as-its-translator-model-17993.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/how-to-shrink-a-language-model-without>)

Author: ByteByteGo

Published: 2026-09-02T15:31:12Z

Content type: tutorial

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [embedding](<https://devfeed.tech/tags/embedding.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [model](<https://devfeed.tech/tags/model.md>), [rag](<https://devfeed.tech/tags/rag.md>), [retrieval-augmented-generation](<https://devfeed.tech/tags/retrieval-augmented-generation.md>)

### AI overview

This article explains how embedding models support retrieval-augmented generation by translating words into vectors and controlling the search for relevant information before an answer is generated. It discusses why poor retrieval can produce incorrect answers even when the language model is capable, and outlines considerations for evaluating and selecting embedding models.

### Source excerpt

In this article, we're going to look at how this embedding model works in an RAG setup and what makes it such a critical part of the system.

## Techniques for Shrinking Language Models

DevFeed: [Techniques for Shrinking Language Models](<https://devfeed.tech/articles/how-to-shrink-a-language-model-without-making-it-too-dumb-17994.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/how-to-shrink-a-language-model-without-295>)

Author: ByteByteGo

Published: 2026-09-01T15:30:41Z

Content type: tutorial

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>)

Tags: [hardware](<https://devfeed.tech/tags/hardware.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [techniques](<https://devfeed.tech/tags/techniques.md>)

### AI overview

This tutorial explains why large language models can exceed consumer graphics-memory capacity and introduces three techniques intended to reduce model size while preserving output quality.

### Source excerpt

Models have grown roughly 100-fold in a few years, while consumer graphics memory has roughly doubled. It's not just a matter of tightening things up to make them fit.

## What Happens Inside an AI Chatbot Between Enter and the First Word?

DevFeed: [What Happens Inside an AI Chatbot Between Enter and the First Word?](<https://devfeed.tech/articles/what-happens-inside-an-ai-chatbot-between-enter-and-the-first-word-18000.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/what-happens-inside-an-ai-chatbot>)

Author: ByteByteGo

Published: 2026-08-31T15:31:20Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [context](<https://devfeed.tech/topics/context.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [Tool](<https://devfeed.tech/topics/tool.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [caching](<https://devfeed.tech/tags/caching.md>), [chat](<https://devfeed.tech/tags/chat.md>), [context](<https://devfeed.tech/tags/context.md>), [llm](<https://devfeed.tech/tags/llm.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [tools](<https://devfeed.tech/tags/tools.md>)

### AI overview

This article explains what happens inside an AI chatbot between submitting a follow-up question and receiving the first generated word. It covers prompt assembly, safety checks, token processing, conversation history, shared computing, prefill and decode, caching, streaming, guardrails, and tool execution.

### Source excerpt

In this article, we are going to look at this entire journey in detail.

## How Speculative Decoding Can Make LLM Generation 2-3 Times Faster

DevFeed: [How Speculative Decoding Can Make LLM Generation 2-3 Times Faster](<https://devfeed.tech/articles/how-to-make-llms-3x-faster-17992.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/how-to-make-llms-3x-faster>)

Author: ByteByteGo

Published: 2026-08-26T15:30:34Z

Content type: tutorial

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [decoding](<https://devfeed.tech/tags/decoding.md>), [generation](<https://devfeed.tech/tags/generation.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [llms](<https://devfeed.tech/tags/llms.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

This tutorial explains speculative decoding, in which a smaller model proposes candidate tokens and a larger model evaluates them in a single forward pass. It covers autoregressive generation, GPU utilization, candidate acceptance and rejection, output-quality preservation, acceptance rates, draft sources, and when the technique may stop helping.

### Source excerpt

In this article, we will look at how speculative decoding works.

## Researchers Found That Encrypted AI Reasoning Blocks Can Be Replayed to Reveal Hidden Reasoning

DevFeed: [Researchers Found That Encrypted AI Reasoning Blocks Can Be Replayed to Reveal Hidden Reasoning](<https://devfeed.tech/articles/how-to-steal-an-ai-model-s-private-thoughts-17995.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/how-to-steal-an-ai-models-private>)

Author: ByteByteGo

Published: 2026-08-25T15:31:09Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Security](<https://devfeed.tech/topics/security.md>), [Chain-of-thought](<https://devfeed.tech/topics/chain-of-thought.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [anthropic](<https://devfeed.tech/topics/anthropic.md>), [Google](<https://devfeed.tech/topics/google.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [anthropic](<https://devfeed.tech/tags/anthropic.md>), [google](<https://devfeed.tech/tags/google.md>), [openai](<https://devfeed.tech/tags/openai.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [research](<https://devfeed.tech/tags/research.md>), [security](<https://devfeed.tech/tags/security.md>)

### AI overview

The article describes research testing whether encrypted reasoning blocks returned by Anthropic, OpenAI, and Google protect hidden model reasoning. It reports that the blocks can be replayed into a cheaper model from the same family, revealing the reasoning in plaintext, and outlines the extraction method, attack vectors, and proposed fixes.

### Source excerpt

In August 2026, a team at MATS Research, the ELLIS Institute Tübingen, and the Max Planck Institute for Intelligent Systems wanted to test whether the encrypted reasoning blocks that Anthropic, OpenAI, and Google hand back to clients actually keep that reasoning private.

## Why Code Verification Matters More Than Ever in the Age of AI

DevFeed: [Why Code Verification Matters More Than Ever in the Age of AI](<https://devfeed.tech/articles/why-code-verification-matters-more-than-ever-in-the-age-of-ai-18001.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/why-code-verification-matters-more>)

Author: ByteByteGo

Published: 2026-08-24T15:31:01Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Code](<https://devfeed.tech/topics/code.md>), [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-assisted-coding](<https://devfeed.tech/tags/ai-assisted-coding.md>), [code](<https://devfeed.tech/tags/code.md>), [cto](<https://devfeed.tech/tags/cto.md>), [developers](<https://devfeed.tech/tags/developers.md>), [verification](<https://devfeed.tech/tags/verification.md>)

### AI overview

The article explains why code verification is becoming more important as AI-assisted coding makes software production faster. It discusses the growing review burden, low confidence in AI-generated code, and research indicating that increased AI use can create pressure on delivery stability and developer productivity.

### Source excerpt

In this article, we will look at how code verification works, why the rise of AI-generated code puts more pressure on it, along with the extremely useful insights from Andrea on what the future may look like.

## EP223: Ollama vs vLLM vs SGLang

DevFeed: [EP223: Ollama vs vLLM vs SGLang](<https://devfeed.tech/articles/ep223-ollama-vs-vllm-vs-sglang-17985.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/ep223-ollama-vs-vllm-vs-sglang>)

Author: ByteByteGo

Published: 2026-08-22T15:31:34Z

Content type: comparison

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Ollama](<https://devfeed.tech/topics/ollama.md>), [sglang](<https://devfeed.tech/topics/sglang.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [API](<https://devfeed.tech/topics/api.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [Agent Skills](<https://devfeed.tech/topics/agent-skills.md>)

Tags: [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [api](<https://devfeed.tech/tags/api.md>), [cache](<https://devfeed.tech/tags/cache.md>), [models](<https://devfeed.tech/tags/models.md>), [ollama](<https://devfeed.tech/tags/ollama.md>), [sglang](<https://devfeed.tech/tags/sglang.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

This comparison explains how Ollama, vLLM, and SGLang handle requests for open-weight models. Ollama uses a FIFO queue and pre-quantized GGUF models for local development and prototyping; vLLM uses continuous batching and PagedAttention for high-traffic serving; and SGLang uses prefix-aware scheduling and RadixAttention for agents, multi-turn chats, and structured outputs.

### Source excerpt

To use open-weight models on your machine, you have three main options: Ollama, vLLM, and SGLang. But each engine handles requests differently.

## Schema Evolution: Changing the Contract Without Breaking What Runs

DevFeed: [Schema Evolution: Changing the Contract Without Breaking What Runs](<https://devfeed.tech/articles/schema-evolution-changing-the-contract-without-breaking-what-runs-17998.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/schema-evolution-changing-the-contract>)

Author: ByteByteGo

Published: 2026-08-20T15:32:18Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [schema-evolution](<https://devfeed.tech/topics/schema-evolution.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [evolution](<https://devfeed.tech/tags/evolution.md>), [schema](<https://devfeed.tech/tags/schema.md>), [schema-evolution](<https://devfeed.tech/tags/schema-evolution.md>)

### AI overview

The article examines schema evolution and strategies for managing changes to data schemas.

### Source excerpt

In this article, we will look at schema evolution and strategies for the same.

## GraphRAG: How AI Answers Questions Hidden Across Many Documents

DevFeed: [GraphRAG: How AI Answers Questions Hidden Across Many Documents](<https://devfeed.tech/articles/graphrag-how-ai-answers-questions-hidden-across-many-documents-17988.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/graphrag-how-ai-answers-questions>)

Author: ByteByteGo

Published: 2026-08-19T15:31:18Z

Content type: tutorial

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Graphs](<https://devfeed.tech/topics/graphs.md>), [Retrieval-Augmented Generation](<https://devfeed.tech/topics/retrieval-augmented-generation.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [graphs](<https://devfeed.tech/tags/graphs.md>), [knowledge-graphs](<https://devfeed.tech/tags/knowledge-graphs.md>), [rag](<https://devfeed.tech/tags/rag.md>), [retrieval-augmented-generation](<https://devfeed.tech/tags/retrieval-augmented-generation.md>), [search](<https://devfeed.tech/tags/search.md>)

### AI overview

This tutorial explains how GraphRAG addresses questions whose answers emerge from patterns across an entire document collection, contrasting it with standard RAG, which is designed to retrieve information from specific documents.

### Source excerpt

GraphRAG was designed to handle the second kind of questions, and we are going to learn more about it in this article.

## Thinking Machines' Inkling: Architecture and Customization Choices

DevFeed: [Thinking Machines' Inkling: Architecture and Customization Choices](<https://devfeed.tech/articles/the-new-american-ai-model-designed-to-be-customized-17999.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/the-new-american-ai-model-designed>)

Author: ByteByteGo

Published: 2026-08-18T15:30:36Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [context window](<https://devfeed.tech/topics/context-window.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [interfaces](<https://devfeed.tech/topics/interfaces.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [mixture-of-experts](<https://devfeed.tech/tags/mixture-of-experts.md>), [model](<https://devfeed.tech/tags/model.md>), [real-time](<https://devfeed.tech/tags/real-time.md>)

### AI overview

The article examines the architecture and design choices behind Thinking Machines' Inkling model, including its mixture-of-experts structure, local and global attention, position encoding, multimodal inputs, and adjustable thinking effort. It also notes that Inkling is the company's first model trained from scratch and that its weights are available on Hugging Face under an Apache 2.0 license.

### Source excerpt

In this article, we will work through the various choices Thinking Machines made while building Inkling.