{ "schema_version": "1.1", "id": "atlas-model-selection", "slug": "model-selection", "title": "Model Selection", "url": "https://feed7.dev/atlas/model-selection", "current_answer": null, "implementation_consequence": null, "agent_context": null, "confidence": "auto_collected", "last_verified": null, "last_updated": null, "evidence": [ { "schema_version": "1.1", "id": "p10", "slug": "evals-workshop-aie", "url": "https://feed7.dev/p/evals-workshop-aie", "title": "Building eval sets that survive model swaps — AI Engineer workshop", "why_included": "Eval sets usually die when you change models. This workshop shows how to write ones that transfer.", "summary": "Behavior-anchored evals: assert on user-visible outcomes, not model phrasing. Includes a template repo and a live migration from GPT to Claude.", "practical_implication": "Rewrite phrasing-based assertions as outcome assertions now — before your next model swap forces it.", "agent_context": "Write evals against user-visible outcomes, not model phrasing. Outcome-anchored evals survive model swaps. Template: given/when/then on behavior, never on wording.", "source": { "name": "AI Engineer", "url": "https://www.youtube.com/watch?v=ai-eng-evals-2026", "published_at": "2026-06-21T00:00:00.000Z" }, "source_class": "video", "content_type": "Workshop", "layer": "benchmark", "domains": [ "coding" ], "topics": [ "agent-evals", "model-selection" ], "verification": { "status": "transcript_verified", "label": "Transcript Verified", "method": "transcript_review", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "Evergreen", "published_at": "2026-06-21T00:00:00.000Z", "modified_at": "2026-06-21T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/evals-workshop-aie", "json": "https://feed7.dev/p/evals-workshop-aie.json", "markdown": "https://feed7.dev/p/evals-workshop-aie.md" } }, { "schema_version": "1.1", "id": "auto-bb03b50141", "slug": "introducing-cursor-router-bb03b50141", "url": "https://feed7.dev/p/introducing-cursor-router-bb03b50141", "title": "Introducing Cursor Router", "why_included": "Evaluate model routing by cost per shipped change, using repository-specific quality, latency, and code-retention signals.", "summary": "Cursor Router classifies coding requests and selects models by task and cost. Cursor reports lower spend in production tests, but the strongest evidence is limited to its own traffic and metrics.", "practical_implication": "Teams should test routing against cost per shipped change, not token price alone. Cursor’s classifier considers query, context, complexity, domain, model behavior, and cache misses; admins can set defaults and restrict models or modes.", "agent_context": "Cursor Router was trained on **600k+ live requests** and evaluated through A/B tests covering **millions of requests**. Cursor reports frontier-level satisfaction at about **60% lower cost** and offers Intelligence, Balance, and Cost modes.\n\nTeams should test routing against cost per shipped change, not token price alone. Cursor’s classifier considers query, context, complexity, domain, model behavior, and cache misses; admins can set defaults and restrict models or modes.\n\nThe reported **30–50% early-access savings** came from three high-volume accounts and used Opus 4.8 pricing as the counterfactual. Quality relies on Cursor’s satisfaction and code keep-rate signals, so results may not transfer to other harnesses or workloads.", "source": { "name": "Cursor", "url": "https://cursor.com/blog/router", "published_at": "2026-07-22T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "tools", "domains": [ "coding" ], "topics": [ "model-selection", "coding-agents", "enterprise" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-22T00:00:00.000Z", "modified_at": "2026-07-22T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/introducing-cursor-router-bb03b50141", "json": "https://feed7.dev/p/introducing-cursor-router-bb03b50141.json", "markdown": "https://feed7.dev/p/introducing-cursor-router-bb03b50141.md" } }, { "schema_version": "1.1", "id": "auto-6d964f10c3", "slug": "how-gpt-5-6-fuses-frontier-intelligence-with-frontier-ef-6d964f10c3", "url": "https://feed7.dev/p/how-gpt-5-6-fuses-frontier-intelligence-with-frontier-ef-6d964f10c3", "title": "How GPT-5.6 fuses frontier intelligence with frontier efficiency", "why_included": "The supplied material offers no prices, benchmarks, latency, or task-level evidence to guide a routing or migration decision.", "summary": "OpenAI positions GPT-5.6 as delivering more useful output per dollar across inference and agent workflows. The supplied material has no metrics for judging routing or migration decisions.", "practical_implication": "Builders should evaluate the model on complete agent runs, including reasoning and tool calls, rather than comparing only per-token pricing.", "agent_context": "OpenAI says **GPT-5.6** improves efficiency across **models, inference, and agentic workflows**, with more useful output delivered per dollar.\n\nBuilders should evaluate the model on complete agent runs, including reasoning and tool calls, rather than comparing only per-token pricing.\n\nThe supplied material contains no prices, benchmarks, latency figures, or task-level evidence, so it does not establish which workloads benefit or by how much.", "source": { "name": "OpenAI", "url": "https://openai.com/index/gpt-5-6-frontier-intelligence-efficiency", "published_at": "2026-07-29T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Official Release", "layer": "model", "domains": [], "topics": [ "model-selection", "reasoning" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-29T00:00:00.000Z", "modified_at": "2026-07-29T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/how-gpt-5-6-fuses-frontier-intelligence-with-frontier-ef-6d964f10c3", "json": "https://feed7.dev/p/how-gpt-5-6-fuses-frontier-intelligence-with-frontier-ef-6d964f10c3.json", "markdown": "https://feed7.dev/p/how-gpt-5-6-fuses-frontier-intelligence-with-frontier-ef-6d964f10c3.md" } }, { "schema_version": "1.1", "id": "auto-b3425f8b3f", "slug": "how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f", "url": "https://feed7.dev/p/how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f", "title": "How Cursor Router chooses the right model for the task", "why_included": "Route models using real task outcomes, corrections, cache costs, confidence thresholds, and an explicit per-turn budget.", "summary": "Cursor Router learns task complexity and model fit from production behavior, showing why agent routing should include correction signals, cache costs, and per-task performance.", "practical_implication": "Builders implementing model routing should learn from real task outcomes, including corrections and task progression, rather than rely only on benchmarks. Include token use, cache misses, model-switching costs, confidence thresholds, and an explicit per-turn budget.", "agent_context": "Cursor routes each turn in two stages: Compass estimates complexity, then a production-derived taxonomy selects among eligible frontier models. **Auto Intelligence costs 68% less than Fable**, while **Auto Balance costs 41% less than Opus 4.8** and reports higher satisfaction.\n\nBuilders implementing model routing should learn from real task outcomes, including corrections and task progression, rather than rely only on benchmarks. Include token use, cache misses, model-switching costs, confidence thresholds, and an explicit per-turn budget.\n\nCursor's satisfaction measure is inferred from subsequent user behavior, so it is a proxy rather than a direct quality score. The reported results come from Cursor traffic and may not transfer to another workload, user population, or pricing mix.", "source": { "name": "Cursor", "url": "https://cursor.com/blog/how-cursor-router-works", "published_at": "2026-08-06T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "agent", "domains": [ "coding", "data" ], "topics": [ "model-selection", "harness-engineering", "agent-reliability" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-06T00:00:00.000Z", "modified_at": "2026-08-06T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f", "json": "https://feed7.dev/p/how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f.json", "markdown": "https://feed7.dev/p/how-cursor-router-chooses-the-right-model-for-the-task-b3425f8b3f.md" } } ], "conflicting_sources": [], "superseded_claims": [], "corpus_evidence": [ { "title": "Post-Training Language Models for Gold-Medal Performance in Coding Competitions", "url": "https://arxiv.org/abs/2609.02849v1", "source_name": "arXiv", "published_at": "2026-09-02T17:33:37+00:00", "summary": "A coding-specialized model paired post-training with an iterative generate-evaluate-refine loop to exceed the top IOI 2026 human score. The reusable idea is feedback-driven test-time search." }, { "title": "UE5M3 FP4 Block Scaling for Stable Language Model Pretraining", "url": "https://arxiv.org/abs/2609.02846v1", "source_name": "arXiv", "published_at": "2026-09-02T17:32:07+00:00", "summary": "A UE5M3 block-scaling recipe trained an 8B model in FP4 without Hadamard transforms or BF16 final layers, while reporting better losses and downstream estimates than the compared recipe." }, { "title": "Gemini 3.8 Flash now available on AI Gateway", "url": "https://vercel.com/changelog/gemini-3-8-flash-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-09-02T00:00:00+00:00", "summary": "Gemini 3.8 Flash brings multimodal input, tool calling, web search, and default reasoning to coding agents through Vercel. Its temporary 50% discount runs through December 31." }, { "title": "Muse Spark 1.3 now available on AI Gateway", "url": "https://vercel.com/changelog/muse-spark-1-3-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-09-02T00:00:00+00:00", "summary": "Muse Spark 1.3 gives coding agents a 1M-token, multimodal model through Vercel, with a cheaper contributor tier that permits Meta to train on submitted inputs and outputs." }, { "title": "The Structure of Quantization Damage in LLMs: Why the Next Bit Should Be Spent Globally", "url": "https://arxiv.org/abs/2609.01587v1", "source_name": "arXiv", "published_at": "2026-09-01T17:53:41+00:00", "summary": "When quantizing an open model, spend a small extra precision budget across the network before protecting a few “important” layers; causal tests found the damage was usually diffuse." }, { "title": "Scaling Near-Optimal SFT-RL Annotation Budget Allocation from Small to Large LLMs", "url": "https://arxiv.org/abs/2609.01573v1", "source_name": "arXiv", "published_at": "2026-09-01T17:39:55+00:00", "summary": "Small proxy models may be enough to choose an SFT-versus-RL annotation split: the paper finds broad near-optimal ranges that transfer to larger models." }, { "title": "From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix", "url": "https://arxiv.org/abs/2609.01572v1", "source_name": "arXiv", "published_at": "2026-09-01T17:39:26+00:00", "summary": "A production-derived post-training recipe consolidated more than 200 internal apps onto one self-hosted model by training separate experts for distinct quality gaps, then merging them." }, { "title": "Qwen 3.8 Max 0902 now available on AI Gateway", "url": "https://vercel.com/changelog/qwen-3-8-max-0902-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-09-01T00:00:00+00:00", "summary": "Vercel’s gateway now exposes a pinned Qwen snapshot aimed at larger coding projects and longer agent runs, with a dated model ID that prevents silent upgrades." }, { "title": "Claude Fable 5.1 now available on AI Gateway", "url": "https://vercel.com/changelog/claude-fable-5-1-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-09-01T00:00:00+00:00", "summary": "Claude Fable 5.1 reaches Vercel AI Gateway with ordered fallbacks for classifier refusals, but its 30-day retention policy rules out zero-data-retention workloads." }, { "title": "When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning", "url": "https://arxiv.org/abs/2608.31118v1", "source_name": "arXiv", "published_at": "2026-08-31T17:30:05+00:00", "summary": "A controlled ontology-learning study finds model size is a weak selector on its own. Dense 27B models beat larger sparse models on one task, while MoE models led another." }, { "title": "Agentic Sites: Building Hyper Personalized Websites — Carlos Sanchez, Adobe", "url": "https://www.youtube.com/watch?v=jebp4V0vh30", "source_name": "AI Engineer", "published_at": "2026-08-29T17:00:17+00:00", "summary": "Adobe’s prototype assembles intent-specific page blocks from existing site content in roughly a second, making model latency and per-site evaluation part of frontend architecture." }, { "title": "The Half Life of Agent Infrastructure — Ben Kus, Box", "url": "https://www.youtube.com/watch?v=sM1iYgz93HI", "source_name": "AI Engineer", "published_at": "2026-08-29T14:30:22+00:00", "summary": "Agent architectures are expiring quickly. Keep model, search, and orchestration choices replaceable, and evaluate platforms by how well they handle repeated change." }, { "title": "Hy4 Preview now available on AI Gateway", "url": "https://vercel.com/changelog/hy4-preview-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-08-28T00:00:00+00:00", "summary": "Tencent’s Hy4 Preview is now callable through Vercel AI Gateway and selectable in coding agents, adding an open MoE option with a 1M-token context window." }, { "title": "Consolidating RLVR Capabilities Across Domains: A Deep Dive into Fusion Paradigms", "url": "https://arxiv.org/abs/2608.27409v1", "source_name": "arXiv", "published_at": "2026-08-27T17:38:04+00:00", "summary": "Three ways to combine RLVR domain experts perform similarly on average but diverge by task. Choose Merge for cheap reuse, Mix RL for training from pooled data, and MOPD for preserving expert gains." }, { "title": "Ling 3.0 Flash Fin now available on AI Gateway for free", "url": "https://vercel.com/changelog/ling-3-0-flash-fin-now-available-on-ai-gateway-for-free", "source_name": "Vercel", "published_at": "2026-08-27T00:00:00+00:00", "summary": "Ling 3.0 Flash Fin adds a finance-focused reasoning and tool-calling option to AI Gateway, with separate model IDs for automatic billing or a hard stop after the free period." }, { "title": "Qwen 3.8 Flash now available on AI Gateway", "url": "https://vercel.com/changelog/qwen-3-8-flash-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-08-26T00:00:00+00:00", "summary": "Qwen 3.8 Flash is now selectable in Vercel AI Gateway and coding agents, with text-and-image input, a 1M-token context window, and responses up to 65k tokens." }, { "title": "GLM 5.3 Flash now available on AI Gateway", "url": "https://vercel.com/changelog/glm-5-3-flash-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-08-26T00:00:00+00:00", "summary": "GLM 5.3 Flash joins Vercel AI Gateway with text and vision input, a 1M-token context window, function calling, structured output, and streaming." }, { "title": "Wan 3.0 now available on AI Gateway", "url": "https://vercel.com/changelog/wan-3-0-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-08-25T00:00:00+00:00", "summary": "Wan 3.0 gives AI Gateway one video model ID for text, image, frame, and reference workflows, with async renders up to 30 seconds at 1080p and synchronized audio." }, { "title": "MiniMax M3 and M2.7 are free on AI Gateway", "url": "https://vercel.com/changelog/minimax-m3-and-m2-7-are-free-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-08-25T00:00:00+00:00", "summary": "AI Gateway offers temporary free routes for MiniMax M3 and M2.7, but the -free model IDs hard-fail after September 6; standard IDs preserve provider fallback at normal rates." }, { "title": "Preferences Over Benchmarks: Model Routing — Archana Kamath & Tyler Gillam, DigitalOcean", "url": "https://www.youtube.com/watch?v=FvxY8oPoI8o", "source_name": "AI Engineer", "published_at": "2026-08-22T15:30:18+00:00", "summary": "Per-task model routing cut the demonstrated coding session’s cost from 44¢ to 14¢ with similar completion time, but builders still need workload-specific evals to validate quality." }, { "title": "Asymmetric Capacity Allocation in Self-Refinement Pipelines", "url": "https://arxiv.org/abs/2608.21345v1", "source_name": "arXiv", "published_at": "2026-08-21T17:52:17+00:00", "summary": "Self-refinement pipelines need not use equally capable models: invest capacity in generation and revision, while a small critic may preserve gains at lower compute cost." }, { "title": "CLEAR: Continuous Latent Adapter Routing for Utility-Preserving LLM Safety Alignment", "url": "https://arxiv.org/abs/2608.21278v1", "source_name": "arXiv", "published_at": "2026-08-21T16:36:10+00:00", "summary": "CLEAR conditionally activates a safety adapter instead of applying safety tuning to every prompt, reducing harmful completions while limiting benign-task degradation." }, { "title": "Your Fine-Tuned Model Is Tech Debt: A 50x ROI House of Cards — Dan Bjornn, Lease End", "url": "https://www.youtube.com/watch?v=4loPnxvWWhg", "source_name": "AI Engineer", "published_at": "2026-08-20T16:00:22+00:00", "summary": "Lease End replaced a fine-tuned intent classifier with skills and runtime context, cutting production fixes from about a week to under an hour. Higher API spend was offset by lower maintenance cost." }, { "title": "TokEval: A Tokenizer Evaluation Suite", "url": "https://arxiv.org/abs/2608.18062v1", "source_name": "arXiv", "published_at": "2026-08-18T17:52:52+00:00", "summary": "TokEval links tokenizer properties to language, math, and code performance, offering cheaper screening signals before committing compute to pretraining sweeps." }, { "title": "Where A Small Language Model Helps in Invoice Categorisation, Understood Through Embedding Geometry", "url": "https://arxiv.org/abs/2608.18033v1", "source_name": "arXiv", "published_at": "2026-08-18T17:28:01+00:00", "summary": "A single-GPU SBERT beat the reported zero-shot LLM and vendor baseline for invoice coding, suggesting narrow, private classifiers can outperform broader models with modest local data." }, { "title": "GLM 5.3 now available on AI Gateway", "url": "https://vercel.com/changelog/glm-5-3-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-08-18T00:00:00+00:00", "summary": "GLM 5.3 is available through Vercel AI Gateway for coding agents, retaining a 1M-token context window while claiming better long-horizon engineering with fewer output tokens." }, { "title": "GPT-5.6 Sol is 50% off on AI Gateway for the next month", "url": "https://vercel.com/changelog/gpt-5-6-sol-is-50-off-on-ai-gateway-for-the-next-month", "source_name": "Vercel", "published_at": "2026-08-17T00:00:00+00:00", "summary": "Vercel cut GPT-5.6 Sol pricing by 50% through September 18, making direct AI Gateway runs cheaper across every tier without changing model IDs or agent configs." }, { "title": "You Only Pass Once: Answering and Abstaining Together in a Single Forward Pass of a Frozen Language Model", "url": "https://arxiv.org/abs/2608.14465v1", "source_name": "arXiv", "published_at": "2026-08-14T16:44:35+00:00", "summary": "YOPO combines reasoning steering and answer abstention in one pass of a frozen model. Reconstructing the clean residual avoids interference and beats the paper's two-pass reference at three model sizes." }, { "title": "DARTree: Speculative Diffusion Decoding with Autoregressive Draft Trees", "url": "https://arxiv.org/abs/2608.13524v1", "source_name": "arXiv", "published_at": "2026-08-13T17:43:44+00:00", "summary": "DARTree uses autoregressive correction across diffusion-generated draft trees to accelerate lossless decoding. It reports up to 9.73× local speedup, but production gains depend on the serving stack." }, { "title": "DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data", "url": "https://arxiv.org/abs/2608.13517v1", "source_name": "arXiv", "published_at": "2026-08-13T17:37:53+00:00", "summary": "Mimir v1 is an open 1B-parameter reasoning model trained with permissible post-training data. It is relevant for compact or Danish deployments, though the abstract supplies no benchmark scores." }, { "title": "The builder’s guide to GPT‑5.6", "url": "https://openai.com/index/builders-guide-to-gpt-5-6", "source_name": "OpenAI", "published_at": "2026-08-13T11:00:00+00:00", "summary": "OpenAI’s GPT-5.6 guide focuses on model selection and Responses API features for building agents with lower development and operating costs." }, { "title": "Gemini 3.7 Flash now available on AI Gateway for 50% off", "url": "https://vercel.com/changelog/gemini-3-7-flash-now-available-on-ai-gateway-for-50-off", "source_name": "Vercel", "published_at": "2026-08-13T00:00:00+00:00", "summary": "Gemini 3.7 Flash is on Vercel AI Gateway at 50% off through 2026, with direct setup paths for major coding agents and controls for routing, retries, and spend." }, { "title": "GLM 5.2 free for eve agents through August 27 via Blackbox on AI Gateway", "url": "https://vercel.com/changelog/glm-5-2-free-for-eve-agents-through-august-27-via-blackbox-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-08-13T00:00:00+00:00", "summary": "Eve agents can use GLM 5.2 free through August 27 via Vercel AI Gateway; new agents default to it, while existing agents need a model-setting change." }, { "title": "Adaption Labs: Gradient-Free Continual Learning — Sara Hooker, Adaption", "url": "https://www.youtube.com/watch?v=XEd_SRVHBgU", "source_name": "AI Engineer", "published_at": "2026-08-12T16:30:19+00:00", "summary": "Auto Scientist aims to automate model-training choices across data, alignment, and architecture. The builder-relevant claim is broader recipe search, though frontier training remains compute-heavy and safety stays unresolved." }, { "title": "DeepSeek V4 Pro now runs updated weights on AI Gateway", "url": "https://vercel.com/changelog/deepseek-v4-pro-now-runs-updated-weights-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-08-12T07:00:00+00:00", "summary": "AI Gateway moved DeepSeek V4 Pro’s existing alias to updated weights without a code change. Pinning is documented inconsistently, so verify the dated model ID first." }, { "title": "Introducing Grok 4.6", "url": "https://cursor.com/blog/grok-4-6", "source_name": "Cursor", "published_at": "2026-08-12T00:00:00+00:00", "summary": "Grok 4.6 targets long-running coding and knowledge-work agents, with more self-testing and stronger visual first passes reported by Cursor. API pricing starts at $2 input and $6 output per million tokens." }, { "title": "DeepSeek overtakes Google on volume, cost per token falls 13.6%", "url": "https://vercel.com/blog/deepseek-overtakes-google-on-volume-cost-per-token-falls", "source_name": "Vercel", "published_at": "2026-08-11T04:00:00+00:00", "summary": "Vercel’s July gateway data shows model routing, not list-price cuts, drove a 13.6% drop in average token cost as open-weight models gained production traffic." }, { "title": "Open Source Is Dead. Long Live Open Source. — Saoud Rizwan, Cline", "url": "https://www.youtube.com/watch?v=CoEIs6Xm8m8", "source_name": "AI Engineer", "published_at": "2026-08-07T23:26:51+00:00", "summary": "Cline’s founder argues that cheaper open-weight models become practical when verification lives in the harness, while AI-generated noise and package compromise make open-source trust harder." }, { "title": "Local Models: Trust, Control, Optimization — Carter Abdallah, NVIDIA", "url": "https://www.youtube.com/watch?v=FWMJQDH3iK0", "source_name": "AI Engineer", "published_at": "2026-08-07T02:00:06+00:00", "summary": "Open models let builders retain inference traces, customize the training stack, and reduce dependence on one provider, while closed frontier models remain useful for many workloads." }, { "title": "Compression at the Edge — NVIDIA, Unsloth, HuggingFace, Ollama", "url": "https://www.youtube.com/watch?v=J4_jCrTxMkk", "source_name": "AI Engineer", "published_at": "2026-08-07T01:00:06+00:00", "summary": "Quantization can make large models locally runnable, but builders must evaluate each compressed artifact in its real harness because architecture, precision, speed, and quality interact." } ] }