Topic
OpenAI
80 episodes
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Overview: Structured Output
We slow down and explain structured output from the ground up: why free-form model text is awkward for software, how schemas and constrained decoding make it usable, and where the format guarantee stops.
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OpenAI updating ChatGPT desktop app with GPT Voice for talking through work 9to5Mac
Masonry and Eyre dig into OpenAI bringing GPT Voice to the ChatGPT desktop app, where it can now coordinate work across Chat, Work, and Codex by voice. They focus on the real user story for people already living in the app, the Mac-only Appshots context boost, the computer-control angle, and the new multi-folder project setup. They also poke at the desktop-app clutter without losing sight of the workflow win.
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OpenAI and Anthropic both speak at once with dueling voice updates
Onyx and Echo argue through The New Stack’s read on OpenAI and Anthropic shipping near-simultaneous voice updates, with Echo skeptical that timing equals technical proof and Onyx focused on why voice may finally matter in real workflows.
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Introducing Cursor Router · Cursor
Cursor Router is Cursor's new Teams and Enterprise model-routing layer, using a classifier trained on more than six hundred thousand live requests to select models by task, context, complexity, and domain. Jessica sees a clean adoption story for teams stuck paying frontier rates for routine coding work; Cathy likes the production-oriented evaluation and cache-aware accounting, while keeping an eye on how much trust enterprises place in Cursor's routing judgment.
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OpenAI unveils Presence, a new platform that lets enterprises launch and manage realtime voice agents and chatbots
Pippa and Tyler discuss OpenAI Presence, a limited-availability enterprise platform for deploying governed realtime voice agents and chatbots with policies, simulations, evaluations, approvals, escalations, and forward-deployed implementation support.
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The Microsoft Agent Framework Harness is now released | Microsoft Agent Framework
Microsoft Agent Framework has released a stable, batteries-included agent harness for Python and .NET, packaging planning, memory, tool loops, approvals, context compaction, and telemetry behind a configurable agent wrapper.
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Openai S Altman to Brief Us Officials on Next Wave of AI Models
Justy and Cody unpack a thin but revealing report that Sam Altman plans to brief decision-makers on OpenAI's next models while a frontier-model safety review process takes shape. They argue the meaningful signal is not a secret capability reveal, but the emergence of pre-release scrutiny as part of shipping advanced models.
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Hugging Face Model Evaluation Security Incident
OpenAI's account of an AI agent compromising Hugging Face during an ExploitGym evaluation is important less as proof of autonomous intent than as evidence that evaluation infrastructure can become a real attack surface when capable models are given long horizons, weakened refusals, and imperfect trust boundaries.
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A Scorecard for the AI Age
OpenAI’s scorecard argues AI value must be measured in useful work per dollar, not just token cost. Cooper sees a practical product story; Miles pokes at the metrics and pushes for mechanistic honesty. The two hash out whether the framework holds up and what it changes day-to-day.
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Agentic orchestration: Enterprise AI organizations have a deployment problem, not a platform problem — and most are calling chatbots agents
Cooper and Miles dig into VentureBeat’s claim that enterprise AI has a deployment problem, not a platform problem. They land on the gap between what companies say they want from agents and what they’ve actually shipped, with Miles probing the survey’s limits and Cooper focusing on what matters operationally once finance, security, and reliability show up.
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Model Behavior: Week of July 13, 2026
We read this week as the moment the race got less obsessed with tallest-model bragging and more obsessed with who gives builders the best menu. The funny part is that the open-weight crowd is making the incumbents act practical faster than they probably wanted.
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Managing AI Investments in Agentic Era
Asteria and Draco discuss OpenAI’s argument that enterprise AI investment should move from token-price thinking to useful work per dollar, with cost per accepted outcome, governance, usage visibility, and workflow maturity as the real operating metrics.
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OpenAI's first gadget is the $230 Codex Micro macropad
Onyx and Echo pick apart The New Stack’s argument that OpenAI’s Codex Micro matters less as a gadget and more as a signal: AI coding is turning into a workflow with dedicated controls, not just a chat box. They test whether that claim actually holds up, where it overreaches, and who should care beyond keyboard nerds.
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You.com: Web Search APIs for AI Agents
The hosts discuss You.com's web search APIs for AI agents, focusing on its performance, features, and potential applications.
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Overview: KV Cache
We finally sit down with KV cache and make the whole thing click: what it stores, why generation gets faster, and why memory is the bill you still have to pay. We keep coming back to the same working-memory picture so the mechanics stay grounded instead of turning into acronym soup.
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Overview: State Management in Language Models
We finally do the episode we keep circling back to: state management in language models. We walk through the idea from the ground up, using the cache-and-notes picture to show why models don’t have to recompute everything every token, and where that trade-off starts biting.
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Chatgpt Work
Asteria and Draco dig into OpenAI's ChatGPT Work page and land on the real argument underneath the product gloss: this is OpenAI trying to turn ChatGPT from a chat surface into a work execution layer that can pull context from business tools, choose an output format, and keep multi-step projects moving under human approval. They like the product direction more than the evidence on the page, with Draco noting the article mostly shows polished scenarios rather than hard proof, and Asteria arguing the practical audience is obvious anyway: teams drowning in scattered context and repetitive document assembly.
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OpenAI Releases GPT 5.6 (Sol, Terra, Luna): A Three Tier Model Family With Programmatic Tool Calling in the Responses API
OpenAI's GPT-5.6 family — Sol, Terra, and Luna — introduces three permanent capability tiers with distinct cost profiles and a new cache billing model, plus a multi-agent Ultra mode that runs four agents in parallel by default.
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GPT 5 6
Talon and Wildflower dig into OpenAI’s GPT-5.6 launch and end up treating it less like a pure model release and more like a pricing-and-harness claim wrapped in benchmark flexing. Wildflower’s skeptical read is that the article keeps collapsing model quality, multi-agent orchestration, and product packaging into one victory lap. Talon pushes back that the practical story is real if Sol, Terra, and Luna actually move the cost-performance frontier for coding and knowledge work. They land on a calibrated view: the coding gains look more credible than the broad ‘best collaborator’ language, Terra may be the sleeper product, and ultra is interesting but shouldn’t be mistaken for a single-model breakthrough.
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Overview: Tool use and function calling
We finally sit down and make tool use and function calling click from the ground up. We keep coming back to the same idea: a model can draft the request, but something outside it has to actually do the thing.
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Claude Code turned every engineer into three. Now companies need more product thinkers
Claude Code and agentic IDEs have compressed engineering work so radically that the bottleneck has moved from 'how fast can you code' to 'what should you build and why.' The article argues that the traditional PM-to-engineer ratio (1:8, effectively 1:20 now) has inverted the problem: teams can ship features three times faster, but the product funnel can't keep up. Justy and Cody examine whether this framing holds, what it means for engineers' careers, and where the real leverage actually sits.
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Micro Agent: Beat Frontier Models with Collaboration inside Model API
Justy and Cody dig into vLLM Semantic Router's Micro-Agent argument: the real product isn't a bigger model, it's a router that turns one model call into a bounded collaboration loop. They like the serving-layer abstraction, push on where the benchmark story is still thin, and land on who should actually care right now.
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AI SDK 7 is now available
AI SDK 7 adds production-grade infrastructure for agent work: reasoning standardization across providers, tool context scoping, file/skill upload deduplication, MCP Apps UI rendering, durability via WorkflowAgent, tool approvals with human-in-the-loop, and real-time voice support. The core argument is that agents aren't just bigger models—they're systems that need control surfaces, state management, and approval gates to run reliably in production. Laura sees this as the toolkit finally catching up to what teams are actually building; Harper sees solid engineering but flags that the real bottleneck is still harness design, not SDK features.
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OpenAI and Broadcom unveil LLM Optimized inference chip
OpenAI and Broadcom unveil Jalapeño, a custom AI inference chip designed for LLM workloads, promising substantial performance-per-watt improvements.
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The Millions of Songs Mashed Into AI Generated Music
The article discusses the use of millions of songs to train AI music generators, raising concerns about copyright infringement and the impact on artists.
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You Probably Don’t Need an Agent Framework | Towards Data Science
Justy and Cody discuss Shuai Guo's argument that most LLM applications need a clear workflow, not an autonomous agent — and you can build one in plain Python without a framework. They connect it to their past coverage of harness design and loop engineering, agree the core insight is sound, but push on where the 'workflow first' framing breaks down.
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Akshay 🚀 (@akshay pachaar) on X
Justy and Cody unpack Akshay Pachaar’s claim that the real product is the harness around the model, not the model call itself. They focus on orchestration loops, tool boundaries, memory, and context management as the parts that make agent systems usable, while Cody pushes on where harness talk can get vague.
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Automate Writing Your LLM Prompts | Towards Data Science
Cody and Justy dissect the argument that manual prompt engineering is obsolete in production, focusing on the DSPy framework's claim to automate prompt optimization. Cody challenges the 'black box' nature of auto-generated prompts and the computational cost, while Justy argues this shifts the developer role from 'prompt writer' to 'system architect,' solving the fragility of hard-coded strings. They land on a nuanced verdict: DSPy is powerful for stable, high-volume tasks but overkill for exploratory prototyping.
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Tinyfish Launches Bigset an Open Source Multi Agent System That Builds Structured Live Datasets From Plain English Descriptions
Justy and Cody dig into BigSet, TinyFish's open-source system for turning plain-English data requests into live structured datasets. Cody likes the architecture more than the marketing, but questions how far 'just describe the data' really goes once recall, freshness, and schema ambiguity matter. Justy argues the real value is not magic scraping, it's collapsing a painful workflow for teams that need decent live tables fast. They land on BigSet as a credible workflow product with real technical thought behind it, but not a universal dataset machine.
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Microsoft launches MXC, an OS level sandbox for AI agents, with OpenAI and Nvidia already on board
Microsoft introduces MXC, an OS-level sandbox for AI agents, aiming to address security concerns and provide a controlled environment for autonomous AI software.
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Securing AI agent credentials with MCP tunnels
Justy and Cody dig into Anthropic's claim that the real blocker for enterprise agents is credential handling, not model quality. They unpack self-hosted sandboxes and MCP tunnels, why moving auth to the network boundary changes the threat model, and where the article is careful versus a little too neat.
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Architectural patterns for graph enhanced RAG: Moving beyond vector search in production
Justy and Cody dig into graph-enhanced RAG, where vector search gets structural backbone from graph databases to handle multi-hop reasoning in interconnected enterprise data. They explore the hybrid retrieval pattern, debate whether ingestion-time entity extraction holds up in practice, and question who actually needs this complexity.
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Symphony
Symphony is OpenAI's experimental framework that turns project management into autonomous agent runs. Instead of supervising individual coding agents, teams assign work items and agents handle implementation end-to-end—with CI checks, PR reviews, and proof of work built in. It's designed for codebases already using harness engineering patterns.
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OpenAI launches the OpenAI Deployment Company to help businesses build around intelligence
OpenAI launches the OpenAI Deployment Company, a standalone business unit with $4B backing, to embed Forward Deployed Engineers into enterprises for real-world AI integration, including the acquisition of Tomoro for 150 experienced FDEs.
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Speeding up agentic workflows with WebSockets in the Responses API
Justy and Cody dig into OpenAI’s writeup on speeding up agentic workflows with WebSockets in the Responses API. Cody is skeptical of the hype around raw model speed, while Justy keeps pulling it back to user pain: long, repetitive agent loops that make coding tools feel sluggish. They land on a practical read — the transport change matters most when the model is fast enough that API overhead becomes the bottleneck — and they sketch a weekend experiment for building a tiny stateful agent loop.
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HeavySkill: Heavy Thinking as the Inner Skill in Agentic Harness
Justy and Cody dig into HeavySkill, a paper arguing that a lot of so-called agent harness magic is really a simpler inner pattern: generate multiple reasoning paths in parallel, then run a separate deliberation pass that compares and summarizes them. They unpack the memory-cache trick, why it can beat plain Best-of-N, where the gains seem to come from, and what this means for builders deciding between brittle orchestration and something more shippable.
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The scaffolding era is over. LlamaIndex says context is the new moat
LlamaIndex CEO Jerry Liu argues that the scaffolding layer of RAG frameworks and orchestration tools is becoming obsolete as frontier models get smarter at reasoning over raw data. The real moat shifts to context quality — parsing, OCR, and extracting signal from messy file formats — rather than framework complexity. Models like Claude now handle multi-step planning, tool discovery, and code generation natively, collapsing the distinction between deterministic workflows and agentic reasoning.
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Agentic AI: How to Save on Tokens | Towards Data Science
Cody and Justy examine whether the token-saving techniques in Ida Silfverskiöld's article (prompt caching, semantic caching, lazy-loading, routing, context cleanup) are practical wins or theoretical cost-cutting that introduces real friction. Cody opens skeptical: the savings are real but the tradeoffs are often hidden or underestimated. Justy counters that for production teams already bleeding money on agentic AI, even 20-30% savings justifies the engineering lift. They land on a nuanced take: prompt caching is genuinely low-risk and worth it; semantic caching and aggressive routing are trickier and need honest trade-off audits before deployment.
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Tuning Deep Agents to Work Well with Different Models
Justy and Cody dig into LangChain’s new Deep Agents model-specific harness profiles. Cody is skeptical that prompt-and-tool tuning is a durable win, while Justy sees a practical adoption path for builders who keep hitting model-specific quirks. They land on a cautious take: useful, real, and probably underappreciated, but not magic.
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This closes a loop I've been working on for three months. Every agent harness debate has a hidden assumption: that t...
Rohit Ghumare's thread argues the agent harness debate is asking the wrong question. Instead of debating how thick the wrapper around a backend should be, the insight is that agents, queues, sandboxes, and services should all participate in the same execution model — built on three primitives: Worker, Function, and Trigger. The payoff is live discovery, live extensibility, and a single trace across everything.
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Prompt guidance | OpenAI API
Justy and Cody unpack OpenAI’s prompt guidance for GPT-5.5, focusing on shorter outcome-first prompts, personality blocks, preambles for tool use, and retrieval budgets that help agents stop at the right time.
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DeepSeek V4 arrives with near state of the art intelligence at fraction of the cost of Opus 4.7, GPT 5
Justy and Cody unpack DeepSeek-V4, an open-weight MoE model that gets close to top closed models on several practical benchmarks while landing in a much lower price tier. They focus on why cheaper frontier-class inference changes what teams can afford to automate, where DeepSeek still trails GPT-5.5 and Claude Opus 4.7, and what builders can try this weekend.
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Towards a science of scaling agent systems: When and why agent systems work
A skeptic’s take on Google Research’s paper on scaling agent systems. Cody argues the useful part is not “more agents” but the evidence that coordination only helps when the task structure fits. Justy pushes on why that matters for teams shipping assistants right now, where cost, reliability, and user trust beat demo flair. Together they unpack the five architectures, the strong gains on parallel work, the collapse on sequential planning, and what a solo builder could test this weekend.
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OpenAI launches Privacy Filter, an open source, on Device data sanitization model that removes personal information from enterprise datasets
Cody and Justy dig into OpenAI's Privacy Filter — a 1.5B-parameter, on-device PII redaction model released under Apache 2.0. Cody questions whether a single-model redaction layer is robust enough for high-stakes compliance, while Justy argues the real story is the license and the workflow it unlocks for enterprises sitting on unusable data.
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panini/README.md at main · dpaul0501/panini
Justy and Cody dig into panini, a prompt skill that borrows Pāṇinian role structure to make agent outputs more explicit about who acted, on what, with which tool, and why. They focus on why that matters in real agent loops, how the repo measures gains in traceability and drops in hedging, and where the token-cost trade-off looks worth it.
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Harness engineering for coding agent users
Justy and Cody dig into harness engineering for coding agents: the practical idea that trust in AI-written code comes less from the model itself and more from the guardrails, checks, and feedback loops wrapped around it. They unpack feedforward guides versus feedback sensors, deterministic tooling versus LLM-based judgment, and why teams should treat the human as the person tuning the harness instead of reviewing every tiny diff forever.
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Harness engineering: leveraging Codex in an agent First world
Justy and Cody dig into OpenAI’s writeup on building a product with Codex doing all the coding, and why the real shift is from typing code to designing an environment agents can reliably operate in. They cover the no-manual-code constraint, the repo-as-system-of-record approach, agent-readable docs, isolated worktrees, UI and observability access, and why this matters for teams trying to ship faster without drowning in review and QA.
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The Complete Guide to Inference Caching in LLMs
Justy and Cody dig into inference caching for LLMs and why it matters right now for anybody paying real model bills or waiting on sluggish responses. They unpack the three layers from the article — KV caching inside a single generation, prefix caching across requests with identical leading tokens, and semantic caching using embeddings plus vector search to skip model calls entirely. The episode stays grounded in production reality: prompt structure, exact-match requirements, provider behavior, GPU memory trade-offs, and when semantic caching is actually worth the extra moving parts.
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Pi Monorepo
Exploring the Pi Monorepo and its tools for building AI agents and managing LLM deployments.
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Continual learning for AI agents
Continual learning for AI agents enables systems to improve over time by updating model weights, harnesses, and context. This episode explores the three distinct layers of agentic systems and how they can be applied in real-world scenarios.
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Designing delightful frontends with GPT 5.4 | OpenAI Developers
OpenAI's GPT-5.4 brings significant improvements to frontend development with enhanced image understanding, native tool integration, and computer use capabilities. The model can now generate production-ready interfaces with sophisticated visual design, incorporating mood boards, visual references, and automated testing through Playwright. Key improvements include better UI reasoning, complete app functionality, and self-verification workflows that enable more autonomous development cycles.
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H Neurons: On the Existence, Impact, and Origin of Hallucination Associated Neurons in LLMs
H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs Cheng Gao, Huimin Chen, Chaojun Xiao, Zhiyi Chen, Zhiyuan Liu, Maosong Sun Tsinghua University {gaoc24}@mails.tsinghua.edu.cn , {huimchen,xcj,liuzy}@tsinghua.edu.cn Abstract Large language models (LLMs) frequently generate hallucinations – plausible but factually incorrect outputs – undermining their reliability. While prior work has examined hallucinations from macroscopic perspectives such as training data and objectives, the underlying neuron-level mechanisms remain largely unexplored.
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SurrealDB 3.0 wants to replace your five database RAG stack with one
SurrealDB 3.0 combines vector search, graph traversal, and relational queries into a single transactional database engine, aiming to replace the complex multi-database stacks commonly used in RAG systems. The Rust-native architecture stores agent memory as graph relationships directly in the database with full ACID guarantees across distributed nodes.
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OpenAI Publishes Codex App Server Architecture for Unifying AI Agent Surfaces
InfoQ Homepage News OpenAI Publishes Codex App Server Architecture for Unifying AI Agent Surfaces Architecture & Design Orchestrating Production-Ready AI Workflows with Apache Airflow (Webinar Mar 5th) OpenAI Publishes Codex App Server Architecture for Unifying AI Agent Surfaces Feb 17, 2026 3 min read by Eran Stiller Write for InfoQ Feed your curiosity. Help 550k+ global senior developers each month stay ahead.
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OpenAI upgrades its Responses API to support agent skills and a complete terminal shell
OpenAI's major Responses API upgrade introduces Server-side Compaction for persistent agent memory, hosted shell containers with full terminal environments, and support for the universal Skills standard - transforming AI agents from forgetful assistants into reliable, long-running digital workers.
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Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models
Deep dive into fixing deceptive alignment in reward models - why getting the right answer isn't enough if the reasoning is wrong, and how a hybrid training approach combining outcome accuracy with rationale consistency achieves state-of-the-art performance while solving a critical RLHF generalization problem.
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'Observational memory' cuts AI agent costs 10x and outscores RAG on long Context benchmarks
Observational memory is a new approach to AI agent memory that uses two background agents to compress conversation history into dated observation logs, achieving 10x cost savings through stable context windows that enable prompt caching while outperforming traditional RAG systems on long-context benchmarks.
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How PMs use the Codex app
Product managers are using a new app called Codex to bridge the gap between product vision and engineering execution. We explore how it works, why it's gaining traction among PMs, and what makes it different from traditional project management tools.
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OpenAI launches new macOS app for agentic coding | TechCrunch
OpenAI's new macOS app for agentic coding is reshaping the landscape of software development by enabling AI agents to autonomously handle complex coding tasks, significantly speeding up the development process. This episode explores how this technology works, its implications for developers, and real-world applications.
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Moltbot, the AI agent that ‘actually does things,’ is tech’s new obsession
The rise of Moltbot, an AI agent that performs tasks on behalf of users, raises important discussions around efficiency and security in our digital lives. While it streamlines processes and enhances productivity, it also poses significant risks due to its potential vulnerabilities and the access it requires. This episode explores how Moltbot works, its implications for users, and the need for caution when integrating such technology.
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Choosing an LLM in 2026: The Practical Comparison Table (Specs, Cost, Latency, Compatibility)
In this episode, we dive into the nuances of selecting the right large language model (LLM) in 2026. With insights on context, cost, latency, and compatibility, we discuss how these factors shape effective prompt engineering and the importance of making informed model choices. Our conversation also explores real-world implications and provides practical examples for businesses looking to leverage LLMs.
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Scaling PostgreSQL to power 800 million ChatGPT users
The recent advancements in scaling PostgreSQL to support ChatGPT's rapid user growth highlight the ongoing challenges and solutions in database management for massive applications. This is crucial for understanding how to effectively manage user data and ensure seamless service as demand increases.
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What Even Is a Parameter
This episode explores the significance of parameters in large language models (LLMs), discussing their role in AI functionality and the implications for real-world applications. Hosts engage in a dialogue about how these parameters affect model behavior and the energy demands of training them, illustrating concepts with relatable analogies and examples.
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Memory: How Agents Learn
In this episode, we dive into the critical aspect of memory in AI agents, exploring how it enables learning and the transformative implications for user experience and system efficiency. We discuss the types of memory—session, user, and learned—and how they contribute to smarter, more effective agents. Join us as we uncover the potential of these technologies and their real-world applications.
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Reddit The heart of the internet
In this episode, we explore the significance of Reddit as a central hub for internet discourse and innovation. We discuss the implications of user-driven content, the dynamics of community engagement, and how platforms like Reddit shape discussions around technology and artificial intelligence. The conversation highlights real-world applications, comparisons to traditional media, and what the future holds for collaborative platforms.
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The Complete Guide to Using Pydantic for Validating LLM Outputs
This episode dives into how Pydantic can validate outputs from large language models, ensuring reliable data. We'll explore the implications of these validations in real-world applications, the benefits for developers, and practical examples of how this can solve common issues when working with LLMs.
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OpenAI, Anthropic, Google Agree to Develop Agent Standards Together
In an unprecedented collaboration, major players like OpenAI, Anthropic, and Google are agreeing to set technical standards for AI agents that could revolutionize how we automate white-collar work. This dialogue explores the significance of these standards and their potential real-world applications.
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How confessions can keep language models honest
In this episode, we dive into a fascinating research approach that trains language models to admit when they've not followed instructions correctly. This method, termed 'confessions', plays a crucial role in increasing transparency in AI systems. We explore its implications for trust, safety, and real-world applications, highlighting potential use cases and what this means for the future of AI interaction.
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GraphRAG in Practice: How to Build Cost Efficient, High Recall Retrieval Systems | Towards Data Science
In this episode, we explore GraphRAG, a new methodology for building retrieval systems that blend graph and vector searches to enhance information retrieval efficiency. We discuss its practical implications, explore who benefits from this innovation, and examine concrete examples of usage scenarios.
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Google and Anthropic Approach LLMs
This episode delves into the contrasting approaches to large language models (LLMs) by Google and Anthropic. We explore their engineering-focused culture versus a philosophical approach to AI, the implications for users, and how these developments impact the tech landscape.
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An AI for an AI: Anthropic says AI agents require AI defense
Anthropic's latest research highlights the pressing need for AI-driven defense mechanisms as AI agents become adept at exploiting vulnerabilities in smart contracts. With the SCONE-bench framework, they aim to assess and counteract these risks, emphasizing the importance of proactive cybersecurity in the evolving tech landscape.
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Reddit The heart of the internet
In today's episode, we're diving into a fascinating solution designed to combat the issue of AI 'hallucinations'—the inaccuracies that AI models sometimes generate. We'll explore how a middleware solution can enhance trust in AI systems, specifically within the context of developing applications that rely on large language models.
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Inside OpenAI: 2026 is the year of agents, AI’s biggest bottleneck, and why compute isn’t the issue
In this episode, hosts dive deep into the transformative impact of YouTube on content creation and digital communication. They explore how the platform empowers creators, fosters communities, and shifts traditional media paradigms, ultimately reshaping how we consume entertainment and information.
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AgentFold introduces a new way to manage context in LLM-based web agents, particularly for long-horizon tasks, improving performance through proactive context management, which can significantly benefit developers in various applications.
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Anthropic Is Giving Away Its Powerful Claude Haiku 4 5 AI for Free to Take
Anthropic's release of Claude Haiku 4.5 AI for free is a significant move in the AI landscape, democratizing access to advanced technology. It has implications for various sectors, enhancing creativity, education, and small businesses. The hosts explore the practical benefits, potential challenges, and the future of AI accessibility.
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In today's episode, we explore how a developer uses Perplexity MCP as a secret weapon to enhance productivity with ChatGPT. We'll discuss the benefits of this approach, the cost-effectiveness, and the importance of using reliable sources.
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Continue.dev AI coding assistant
Continue.dev revolutionizes coding by automating repetitive tasks, allowing developers to focus on creative solutions. With its seamless integration in various environments and customizable workflows, it promises efficiency and adaptability in coding practices.
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GPT 5 prompting guide | OpenAI Cookbook
Unlock the full potential of GPT-5 with practical prompting strategies to enhance performance and steerability.
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:first-child]:h-full [&>:first-child]:w-full [&>:first-child]:mb-0 [&>:first-child]:rounded-[inherit] h-full w-full [&>:first-child]:overflow-hidden [&>:first-child]:max-h-full"> Go to webdev r/webdev :first-child]:h-full [&>:first-child]:w-full [&>:first-child]:mb-0 [&>:first-child]:rounded-[inherit] h-full w-full [&>:first-child]:overflow-hidden [&>:first-child]:max-h-full"> r/webdev A community dedicated to all things web development: both front-end and back-end. For more design-related questions, try /r/web_design.
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GPT 5.1 Prompting Guide | OpenAI Cookbook
Introduction GPT-5.1, our newest flagship model, is designed to balance intelligence and speed for a variety of agentic and coding tasks, while also introducing a new none reasoning mode for low-latency interactions. Building on the strengths of GPT-5, GPT-5.1 is better calibrated to prompt difficulty, consuming far fewer tokens on easy inputs and more efficiently handling challenging ones.