---
title: AI Models & Industry
url: "https://goremy.ai/blog/topic/ai-models-industry"
type: topic
section: AI News & Models
updated: "2026-11-13T04:00:23.831Z"
posts: 108
---

# AI Models & Industry

New AI models reviewed and compared, how they work, and what the labs behind them are betting on.

Answers: What are the best AI models to compare and review right now?

## Posts (108)

- [How to Change ChatGPT's Reasoning Level When It Seems Dumb](https://goremy.ai/blog/articles/chatgpt-reasoning-level-setting) (2026-11-13): If ChatGPT feels worse lately, check its reasoning level. Here is where the setting lives on mobile, what each plan offers, and why the model can't say.
- [Meta Muse Ember vs Claude Haiku 5.5 vs GPT-6 Luna: Small Model Comparison](https://goremy.ai/blog/articles/meta-muse-ember-vs-claude-haiku-gpt-6-luna) (2026-11-12): Meta's Muse Ember matches Haiku 5.5 on the Artificial Analysis index at about a third of the cost. See Automation Bench, OS World results and delegation tips.
- [What Is Solve Intelligence? AI Patent Drafting Software](https://goremy.ai/blog/articles/what-is-solve-intelligence-ai-patent-software) (2026-11-12): Solve Intelligence builds AI for patent attorneys, used by 700+ IP teams and backed by $55M. How it started and why patents resist generic chatbots.
- [How to Run Underdog Woof TTS on a CPU (and Is It Free to Use?)](https://goremy.ai/blog/articles/how-to-run-underdog-woof-tts-on-cpu) (2026-11-12): Underdog Woof is a 40.6 MB English text-to-speech model that runs on a CPU with NumPy. Setup steps, voices, test results and the non-commercial license catch.
- [Jensen Huang and Megaspeed: The Nvidia Chip Probe Explained](https://goremy.ai/blog/articles/nvidia-jensen-huang-china-chips-megaspeed-investigation) (2026-11-12): Nvidia's board is probing a Jensen Huang relationship tied to Megaspeed while 600,000 H200s wait in Hong Kong. The timeline, export rules and stakes.
- [How Good Is Qwen4 Max? Hands-On Tests vs Qwen 3.8](https://goremy.ai/blog/articles/qwen4-max-hands-on-tests) (2026-11-12): Qwen4 Max preview tested on Blender animation, an 82-language naming task, satellite map reasoning and seat belt laws, plus where it struggled.
- [How Did Boston Dynamics Teach Atlas to Roller Skate?](https://goremy.ai/blog/articles/boston-dynamics-atlas-roller-skates-wheels) (2026-11-12): Boston Dynamics put scooter wheels on Atlas, trained rolling in simulation, and landed a 360 spin first try. Here is how it works and why it matters.
- [Underdog Woof TTS Review: How Accurate Is It, Really?](https://goremy.ai/blog/articles/underdog-woof-tts-test-results-accuracy) (2026-11-11): Underdog Woof claims a 1.42% word error rate. Hands-on tests of Juno and Mira on phone numbers, prices, titles and dates show where it slips.
- [How Did Nvidia Build a 2-Bit 74B Model From a 120B One?](https://goremy.ai/blog/articles/how-nvidia-built-2-bit-model-pruning-distillation-qat) (2026-11-10): Nvidia shrank a 120B model to a 74B model with 2-bit experts that fits in 24 GB. Here is the pruning, distillation and QAD recipe, and why it matters.
- [Should You Say Please to AI? Cost, Quality and Habit](https://goremy.ai/blog/articles/do-people-say-please-to-ai) (2026-11-10): People split on saying please and thank you to AI. Here are the reasons: token cost, water use, better answers, habit and hedging against a robot takeover.
- [What Is Anthropic's AECI Benchmark and Why Log-Odds?](https://goremy.ai/blog/articles/aeci-benchmark-log-odds-saturation-explained) (2026-11-10): Anthropic reportedly tracks progress on a composite benchmark in log-odds. Here is how saturation hides gains and why Model 4 is hard to measure.
- [What Did METR's Anthropic Audit Find About Self-Improvement?](https://goremy.ai/blog/articles/anthropic-meter-audit-recursive-self-improvement-redactions) (2026-11-10): METR's audit of Anthropic and an amended S-1 point to AI-led research rising from 1% to 26%, a 1.9x speedup estimate, and heavy redactions.
- [What Is AREX-2? BAAI's 27B Model That Runs Its Own Retry Loop](https://goremy.ai/blog/articles/arex-2-27b-model-runs-its-own-loop) (2026-11-10): AREX-2 is BAAI's 27B open-weights model trained to try, check and revise for hours. See its MLE-Bench Light scores and what it means for harnesses.
- [Inkling 2 Small Review: How Good Is Thinking Machines' MoE?](https://goremy.ai/blog/articles/inkling-2-small-thinking-machines-hands-on-test) (2026-11-09): Inkling 2 Small is a 594B-parameter, 17B-active multimodal MoE. Hands-on tests in HTML, multilingual output, circuits and vision, plus benchmark standing.
- [How Will Data Center Ballot Defeats Affect AI Compute?](https://goremy.ai/blog/articles/data-center-ballot-defeats-ai-compute-impact) (2026-11-09): Local votes blocked data centers while AI use climbs. Here is how that hits neo clouds, big tech, offshore siting, overbuild risk and orbital compute.
- [What Is DSpine? Tencent's Speculative Decoding Draft Model](https://goremy.ai/blog/articles/dspine-qwen3-8-27b-speculative-decoding-explained) (2026-11-09): DSpine is Tencent's roughly 2B draft model for Qwen3.8-27B. See how it speeds up speculative decoding and how it differs from DFlash2 and Eagle 3.
- [What Is the Clover Diffusion LLM and How Fast Is It?](https://goremy.ai/blog/articles/clover-diffusion-llm-35b-speed-test) (2026-11-07): Clover from Diffuse Space AI is a 35B diffusion language model with 3B active parameters. See how it works and how it fared against Qwen 3.6 Flash.
- [What Is Traction? Physical AI Monitoring for Industrial Assets](https://goremy.ai/blog/articles/traction-series-d-physical-ai-machine-monitoring) (2026-11-07): Traction raised a Series D at a $1.4B valuation with over $100M ARR. Here is how its sensors and AI predict failures in industrial machines.
- [How Do Startups Sell to Industrial Enterprises Without Proof?](https://goremy.ai/blog/articles/traction-enterprise-sales-playbook-paid-pilots-onsites) (2026-11-06): How Traction won industrial buyers: 88 site visits in year one, an SDR as first hire, up-front risk assessments and 2,500 on-sites a year.
- [Croton Alpha Stealth Model: Is It Free and Any Good?](https://goremy.ai/blog/articles/croton-alpha-stealth-model-free-test) (2026-11-06): Croton Alpha is a free-to-try stealth model with 1M-token context for subagents. See what is known and how it did on coding, vision and translation tests.
- [What Is Weight Space Programming? MIT's GPT-6 Astra Paper](https://goremy.ai/blog/articles/weight-space-programming-mit-gpt-6-astra) (2026-11-06): MIT's Sparks of Weight Space Programming paper has GPT-6 Astra read and edit another model's weights, lifting a small Qwen's math from 7.9% to 48.1%.
- [What Is Meta Muse? The AI Assistant Behind 10 Million Setups](https://goremy.ai/blog/articles/meta-muse-ai-assistant-10-million-users) (2026-11-06): Meta's Muse hit 10 million setups in six weeks. Here is who built it, how a team under 200 did it, the polish details, and the cost-to-serve questions.
- [What's New in Tencent Hy4? Vision, Computer Use, Benchmarks](https://goremy.ai/blog/articles/tencent-hy4-vision-computer-use-benchmarks) (2026-11-06): Tencent Hy4 adds vision and computer use over the text-only preview, reports a 75.2 Apex Agents score, and is API only for now. Weights are promised.
- [How Is Ripple-65B Built? Dense Thin-Deep vs Mixture of Experts](https://goremy.ai/blog/articles/ripple-65b-thin-deep-dense-architecture-explained) (2026-11-06): Ripple-65B is a dense 80-layer transformer where all 65B parameters run per token. How it differs from MoE, plus its attention tweaks and training.
- [Which AI Business Should You Start? 13 Models Ranked](https://goremy.ai/blog/articles/ai-business-ideas-ranked-worst-to-best) (2026-11-05): A ranking of 13 AI businesses, from AI SaaS and faceless channels to consulting, scored on technical skill, market demand and durability.
- [What Did GPT-6 Astra Do With Raw Model Weights? Results](https://goremy.ai/blog/articles/gpt-6-astra-weight-editing-experiments-results) (2026-11-05): Astra identified games from raw weights, fixed a stuck car by editing 3.5% of a network, and wrote Atari networks from scratch. Here are the numbers and limits.
- [Tencent Open-Source World Model vs Genie 4: Can You Run It?](https://goremy.ai/blog/articles/tencent-hy-world-model-open-source-genie-4-alternative) (2026-11-04): Tencent's open-weight world model claims Genie 4-style interactive generation on one consumer GPU, with minute-long keyboard steering. How it compares.
- [What Is a Looped Transformer? NVIDIA Dory Explained](https://goremy.ai/blog/articles/what-is-looped-transformer-nvidia-dory-explained) (2026-11-03): A looped transformer reruns a shared block of layers per token to gain depth without more weights. Here is how NVIDIA's Dory does it, and what it costs.
- [How Does SenseNova-Loop Get More Depth Without More Parameters?](https://goremy.ai/blog/articles/sensenova-loop-looped-transformer-mhc-explained) (2026-11-03): SenseNova-Loop-9B-Preview reuses 24 layers twice for 56 layer executions from 32 stored layers, with two-stream mHC and 3:1 linear-to-full attention.
- [What Are Subvocal's Silent-Speech Glasses and Do They Work?](https://goremy.ai/blog/articles/subvocal-glasses-silent-speech-radar-499-preorder) (2026-11-03): Subvocal's radar-based glasses claim to turn silent mouthing into text at 200+ wpm for $499 pre-order. Here is how they work and what remains unproven.
- [Will Websites Block Your AI Agent? Three Zones of Access](https://goremy.ai/blog/articles/websites-blocking-ai-agents-three-zones-agent-access) (2026-11-03): Banks, hotels and schools are deciding how AI agents get in. How signed agents, Visa and Bill, Stripe and Google AP2 shape which sites your agent can use.
- [How Is LFM3's Tri-Hybrid Architecture Built and Trained?](https://goremy.ai/blog/articles/lfm3-1-7b-base-training-recipe-architecture) (2026-11-02): How Liquid AI built LFM3-1.7B-Base: 13 gated short convolutions, 5 sliding-window and 6 global attention layers, and a 22T-token recipe to 256K context.
- [What Is the Agent Control Plane and Who Will Own It?](https://goremy.ai/blog/articles/salesforce-microsoft-oracle-openai-agent-control-plane) (2026-11-02): Salesforce, Microsoft, Oracle and OpenAI are opening products to each other. Here is what the agent control plane is and why enterprise vendors want it.
- [What Is Dynamic Creatures' Hugo Robot and When Will It Launch?](https://goremy.ai/blog/articles/dynamic-creatures-hugo-humanoid-robot-theme-parks) (2026-11-02): Hugo is a furry humanoid robot from Dynamic Creatures with 57 joints and 43 actuators, run by a human remotely today, with customer tests planned for 2027.
- [Sonnet 5.5 vs Opus 5.5 vs Fable 5.1: Which Claude Model?](https://goremy.ai/blog/articles/sonnet-5-5-vs-opus-5-5-vs-fable-5-1) (2026-11-01): Haiku, Sonnet, Opus and Fable compared on cost, speed and task fit, plus when Fable 5.1 is worth paying for and how the /advisor command helps.
- [What Does Anthropic's S-1 Reveal About Revenue and Losses?](https://goremy.ai/blog/articles/anthropic-s-1-filing-revenue-loss-ipo-date) (2026-11-01): Anthropic's S-1 shows $4.585B 2025 revenue, over $16B in H1 2026, an $80B net loss, and a listing expected near Nov 16 at $1.5T to $2T.
- [Distillation vs Reinforcement Learning: Can Qwen 4B Match 27B?](https://goremy.ai/blog/articles/distillation-vs-reinforcement-learning-qwen-4b-27b) (2026-10-31): Qwen 27B taught Qwen 4B three ways: teacher answers, probabilities, on-policy. All landed near 58% versus the 64% teacher, but one cut tokens 30%.
- [What Is Broccoli AI? The YC Home Services Startup at $60M ARR](https://goremy.ai/blog/articles/broccoli-ai-home-services-front-office-yc) (2026-10-30): Broccoli uses AI voice, text and SMS to run the front office for home-service contractors. Here is what it does and how its YC founders found the idea.
- [Why Does Intel 14A Use High-NA EUV When TSMC Doesn't?](https://goremy.ai/blog/articles/intel-14a-high-na-euv-vs-tsmc-backside-power) (2026-10-30): Backside power relaxes front-side pitch, so Intel can print 14A layers in one high-NA pass while TSMC sticks to double-patterned EUV. Plus Intel's PDK lessons.
- [FinFET vs Gate-All-Around: Why Did GAA Take 34 Years?](https://goremy.ai/blog/articles/transistor-history-finfet-gate-all-around-explained) (2026-10-30): How transistors moved from planar to FinFET to gate-all-around, and why GAA, demonstrated in 1988, only reached volume production at Samsung in 2022.
- [What Is Clover-35B-A3B? Block Diffusion on a Qwen3.6 MoE](https://goremy.ai/blog/articles/clover-35b-a3b-block-diffusion-architecture-explained) (2026-10-30): How DiffuSpace's Clover derives from Qwen3.6-35B-A3B: bidirectional GDN attention, confidence-threshold decoding, and block-level expert coreset selection.
- [Is NCM Faster Than an LLM? Next Concept Modeling Tested](https://goremy.ai/blog/articles/next-concept-modeling-ncm-stanford-speed-tests) (2026-10-29): Stanford's NCM writes text coarse to fine. Independent DGX Spark tests show about 4x speed for one user, slower batching, and a Sudoku result with a caveat.
- [What Does Anthropic's S-1 Reveal About Enterprise AI Spend?](https://goremy.ai/blog/articles/anthropic-s-1-filing-fortune-500-spending-analysis) (2026-10-29): Anthropic's S-1 shows over half the Fortune 500 as customers, yet under $1 spent per $1,000 of expenses. Here is what the filing says about demand.
- [What Does Anthropic's IPO Filing Reveal About Its Finances?](https://goremy.ai/blog/articles/anthropic-ipo-filing-revenue-compute-commitments) (2026-10-29): Anthropic's IPO filing cites $16.3B in half-year revenue, up to $750B in compute commitments and a class F share with 50.1% of votes. Key figures.
- [Did OpenAI Disprove the Exponential Time Hypothesis?](https://goremy.ai/blog/articles/openai-disproves-exponential-time-hypothesis-ai-math-breakthroughs) (2026-10-29): OpenAI says its model disproved the Exponential Time Hypothesis. Here is what ETH is, how it relates to P vs NP, and what the claim does and doesn't mean.
- [How Does Medical Billing Work and How Is Candid Automating It?](https://goremy.ai/blog/articles/how-candid-health-automates-medical-billing-rcm) (2026-10-29): How US medical billing works, from claims and ICD-10 codes to denials, and how Candid Health uses automation and AI to fix revenue cycle management.
- [ALEF Flying Car Price, Pre-Orders and Delivery Date](https://goremy.ai/blog/articles/alef-flying-car-price-preorders-delivery-date) (2026-10-28): ALEF's flying car starts around $300,000, has 3,600+ pre-orders and targets first deliveries by year-end after its 1,000th test flight. Here is what we know.
- [What Does Anthropic's S-1 Filing Reveal About Its Business?](https://goremy.ai/blog/articles/anthropic-s-1-filing-key-numbers-explained) (2026-10-28): Anthropic's S-1 shows 900% net revenue retention, 11,900 customers above $100K, multi-chip compute across four vendors, and a "closing the loop" thesis.
- [How Good Is GPT-6.1 Terra? Coding and Computer Use Tests](https://goremy.ai/blog/articles/gpt-6-1-terra-hands-on-test-results) (2026-10-28): Hands-on GPT-6.1 Terra results: build times for a browser OS, skate game, subway FPS and wrestling game, plus a robot arm run, pricing and quirks.
- [What Is a CFET? Gate-All-Around and Chip Scaling Limits](https://goremy.ai/blog/articles/cfet-gate-all-around-chip-scaling-limits-explained) (2026-10-27): A CFET stacks N-type and P-type transistors to save space. Learn how gate-all-around and backside power work, and why heat limits 3D chip scaling.
- [Do Chip Fabs Need Clean Rooms? Why Fab Costs Keep Doubling](https://goremy.ai/blog/articles/do-chip-fabs-need-clean-rooms-fab-cost-economics) (2026-10-27): An Intel executive asks whether chip fabs still need today's clean rooms as fab costs double every four to five years and a high-end EUV scanner nears $400M.
- [What Is Harness²? Self-Evolving Agent Harness Explained](https://goremy.ai/blog/articles/harness-squared-self-evolving-agent-harness-explained) (2026-10-27): Harness² lets an agent rewrite its skills, tools, sub-agents and control loop. How propose, probe, compose works and why a smaller model beat a stronger one.
- [Intel and Musk's Terafab: Will New Fabs Ease the Chip Shortage?](https://goremy.ai/blog/articles/intel-terafab-musk-partnership-chip-tool-bottleneck) (2026-10-27): Intel is working with Elon Musk on Terafab. Here is why tools, not buildings, cap chip supply, and why Intel says it will invest more in equipment.
- [Why Are There Only Three Leading-Edge Chip Foundries?](https://goremy.ai/blog/articles/intel-foundry-vs-tsmc-economies-of-scale-capex) (2026-10-26): Fixed costs near 80%, yield leverage, and a capex gap of roughly $20B vs $50B explain why three foundries remain and what Intel must do to catch TSMC.
- [What Is Forward-Deployed Engineering and Why Do AI Labs Use It?](https://goremy.ai/blog/articles/forward-deployed-engineering-openai-anthropic-ai-roi) (2026-10-26): Forward-deployed engineering explained: how OpenAI's deployment company, Anthropic's joint venture and the Thrive stake help firms turn AI into ROI.
- [What Is DecisionNode? A Hands-On Look at Typed Decisions](https://goremy.ai/blog/articles/decisionnode-typed-decision-model-hands-on) (2026-10-26): DecisionNode returns typed answers with calibrated confidence instead of chat text. See tests on image boosters, API routing, the CLI, injection and RAG.
- [What Is the Kling IPO Plan and Who Owns Kling AI?](https://goremy.ai/blog/articles/kling-hong-kong-ipo-alibaba-tencent-baidu-investors) (2026-10-24): Kuaishou's Kling AI video unit plans a Hong Kong IPO next year at about $18B. Kuaishou holds 68%, with Alibaba, Tencent and Baidu as minority backers.
- [Why Is Flint So Fast? MoE, Sliding Window, 4-Bit Output Layer](https://goremy.ai/blog/articles/flint-moe-architecture-explained-sliding-window-quantized-output) (2026-10-24): Flint runs about 10x faster than a dense 27B model. See how 2.5B active parameters, hybrid attention, a 4-bit output layer and a fused kernel do it.
- [What Is Recursive's Eureka Machine and Did It Build Flint?](https://goremy.ai/blog/articles/recursive-eureka-machine-self-improving-ai-lab) (2026-10-24): Recursive, led by Richard Socher with $650M raised, says its Eureka machine helped design Flint's architecture, quantization, and kernels. What is known so far.
- [Matrix Game 4.0 vs Nvidia StreamRender H3: How Do They Render Games?](https://goremy.ai/blog/articles/matrix-game-4-0-vs-nvidia-streamrender-h3) (2026-10-23): Matrix Game 4.0 hands a video model a low-poly render. Nvidia StreamRender H3 hands it a color-coded map. Here is how the two approaches differ.
- [Can Open-Weight Models Run Mythos-Class Cyberattacks?](https://goremy.ai/blog/articles/open-weight-models-ai-cyberattacks-glasswing-patch-planet) (2026-10-22): Open-weight models like GLM 5.3 reportedly match restricted cyber models from Glasswing and Patch the Planet. Here is what it means for the next six months.
- [How Does DeepSeek's DeepInfra Serve 155 Trillion Tokens a Day?](https://goremy.ai/blog/articles/deepseek-deepinfra-inference-engine-explained) (2026-10-22): DeepSeek open-sourced its DeepInfra inference engine. See how prefill/decode split, expert balancing and speculative decoding work, and what you can run.
- [What Does Stepful Do and How Does It Use AI to Train Healthcare Workers?](https://goremy.ai/blog/articles/stepful-series-c-healthcare-training-ai) (2026-10-22): Stepful raised a $55M Series C led by Oak HC/FT. See how its AI tutors, contracts with 39 health systems, and 5-month programs train healthcare workers.
- [What Can Gemini 4 Argon Do? Five Practical Tests Explained](https://goremy.ai/blog/articles/gemini-4-argon-hands-on-tests) (2026-10-22): Five practical prompts for Gemini 4 Argon: game cloning, API vs subscription research, a 3D-printable STL, scheduling logic and a physics sandbox.
- [What Does Sam Altman Say AI Math Discoveries Mean for Science and Coders?](https://goremy.ai/blog/articles/sam-altman-ai-math-discoveries-mathematicians-coders) (2026-10-22): Sam Altman on OpenAI's math proofs, a disease-treatment candidate within years, FDA limits, and why coders got busier instead of obsolete.
- [Dory 1.0 4B Benchmarks: How Do 1, 2 and 4 Loops Compare?](https://goremy.ai/blog/articles/dory-4b-benchmarks-vs-qwen-3-5-gemma-4) (2026-10-22): NVIDIA Dory1.0-4B scores 50.3 to 61.8 on SWE-bench Verified from 1 to 4 loops. See how it compares with Qwen3.5-4B, Qwen3.5-9B and Gemma 4.
- [Do Diffusion LLMs Scale? Mercury 3 vs Claude Haiku 5.5 and GPT-6 Luna](https://goremy.ai/blog/articles/do-diffusion-llms-scale-mercury-3-benchmarks) (2026-10-22): Mercury 3 is a diffusion LLM that hits 1,068 tokens/s. See how it differs from autoregressive models and how it compares with Haiku 5.5 and GPT-6 Luna.
- [What Is Mercury Decide? Inception's Diffusion Decision Model Explained](https://goremy.ai/blog/articles/what-is-mercury-decide-diffusion-decision-model) (2026-10-21): Mercury Decide returns an answer and probability in about 0.16 seconds, now with images. How it works, its independent evals, and how to pair it with Mercury 3.
- [What Is NVIDIA Nemotron Dory 1.0 4B? Looped Model Explained](https://goremy.ai/blog/articles/nvidia-nemotron-dory-1-0-4b-looped-model) (2026-10-21): Dory1.0-4B is NVIDIA's looped 4B language model that reuses a shared layer block to scale compute per token. Architecture, loop counts, and benchmarks.
- [What Is GPT-6 Astra UI Generation? How It Works](https://goremy.ai/blog/articles/what-is-gpt-6-astra-ui-generation-how-it-works) (2026-10-21): GPT-6 Astra generates finished UIs from one prompt and operates software by reading the screen. See how it works, benchmark scores, pricing, and limits.
- [Open Source Vision Model vs Gemini: Which Is Better for Internal Tools?](https://goremy.ai/blog/articles/open-source-vision-model-vs-gemini-which-is-better-for-internal) (2026-10-21): Compare Ling-3.0-flash-VL with Gemini for internal vision tools: benchmark scores, per-seat vs inference cost, data residency, and governance risks.
- [Are SaaS Terms of Service Changing for AI? What Vendors Are Doing](https://goremy.ai/blog/articles/are-saas-terms-of-service-changing-for-ai-what-vendors-are-doing) (2026-10-21): 92% of AI vendor contracts claim data rights beyond what the service needs. See why SaaS terms of service are changing for AI and how to audit yours.
- [Klarna AI vs Human Support: Which Does Klarna Use Now?](https://goremy.ai/blog/articles/klarna-ai-vs-human-support-which-does-klarna-use-now) (2026-10-21): Klarna's AI assistant did the work of 700 agents, yet the company rebuilt a human support layer in 2025. See what changed, why, and how its hybrid model works.
- [Claude Haiku 5.5 vs. Local Models: Which Is Cheaper for Coding?](https://goremy.ai/blog/articles/claude-haiku-5-5-vs-local-models-which-is-cheaper-for-coding) (2026-10-21): Claude Haiku 5.5 vs. local models for coding, compared on API token prices, electricity, and the engineering time each takes to run and maintain for a team.
- [Can AI Solve Complex Math Problems? Where It Works and Fails](https://goremy.ai/blog/articles/can-ai-solve-complex-math-problems-where-it-works-and-fails) (2026-10-21): Can AI solve complex math problems? Top models reached gold-medal scores at the 2025 IMO but still miss basic word problems. Here is where AI math works and where it breaks.
- [AI Decision Models vs. LLMs: Which Should You Use?](https://goremy.ai/blog/articles/ai-decision-models-vs-llms-which-should-you-use) (2026-10-21): AI decision models vs. LLMs compared on how they work, cost, latency, and benchmark results, with guidance on when to use each for routing and classification.
- [Best AI Model for Internal Tools in 2026: GPT-6 Astra vs Claude Opus 5.5](https://goremy.ai/blog/articles/best-ai-model-for-internal-tools-in-2026-gpt-6-astra-vs-claude) (2026-10-21): Compare GPT-6 Astra, Claude Opus 5.5, and open-weight models like DeepSeek V4.1 Flash for internal tools, with pricing, benchmarks, and self-hosting costs.
- [How Does ChatGPT Choose Which Sources to Cite?](https://goremy.ai/blog/articles/how-does-chatgpt-choose-which-sources-to-cite) (2026-10-20): How ChatGPT chooses which sources to cite, from query rewriting and retrieval to ranking by authority and freshness, and why most retrieved pages go uncited.
- [What Is Answer Engine Optimization? How to Get Cited by AI](https://goremy.ai/blog/articles/what-is-answer-engine-optimization-how-to-get-cited-by-ai) (2026-10-20): Ranking #1 on Google doesn't guarantee an AI citation. Learn what AEO is and which tactics work, based on Ahrefs and Princeton data on stats, structure and freshness.
- [How to Structure Content So AI Assistants Cite It](https://goremy.ai/blog/articles/how-to-structure-content-so-ai-assistants-cite-it) (2026-10-20): Learn how to structure content so ChatGPT, Perplexity, and Google AI Overviews cite it, using answer-first sections, FAQs, and real citation data.
- [How to Track Traffic From AI Assistants in GA4 and Server Logs](https://goremy.ai/blog/articles/how-to-track-traffic-from-ai-assistants-in-ga4-and-server-logs) (2026-10-20): Track traffic from AI assistants in GA4 and server logs with a custom source-only channel group, crawler log analysis, and IP-based bot verification.
- [AEO vs SEO: Which Matters More for AI Search Visibility?](https://goremy.ai/blog/articles/aeo-vs-seo-which-matters-more-for-ai-search-visibility) (2026-10-20): AEO vs SEO for AI search visibility: how each earns results, where they differ, and which matters more now. Includes click data and a checklist.
- [Best AI Models for Internal Tools: Dense vs MoE vs Reasoning](https://goremy.ai/blog/articles/best-ai-models-for-internal-tools-dense-vs-moe-vs-reasoning) (2026-10-20): Compare dense, MoE, reasoning, and state-space AI models for internal tools by cost per task, latency, self-hosting hardware needs, and governance.
- [AI Decision Models vs. Execution Models: Which Does What?](https://goremy.ai/blog/articles/ai-decision-models-vs-execution-models-which-does-what) (2026-10-20): Decision models return typed choices and scores; execution models and sandboxed code act on them. Compare latency, cost, and real benchmarks.
- [The Hidden Costs of Cloud Data Centers: Who Really Pays for 'the Cloud'](https://goremy.ai/blog/articles/hidden-costs-of-cloud-data-centers-explained) (2026-10-17): Electric bills, water tables, bot traffic, and egress fees: who actually pays for the cloud. A data-backed look at cloud computing's externalized costs.
- [What Happened to Microsoft's Copilot+ PCs?](https://goremy.ai/blog/articles/what-happened-to-copilot-pcs-the-full-story) (2026-10-08): Copilot+ PCs launched in 2024 with big claims. Two years of weak sales, Recall's security backlash, and enterprise indifference later, here's what actually happened.
- [GPT-6.1 Sol vs. Claude Opus 5.5: Whose Benchmark Do You Trust?](https://goremy.ai/blog/articles/gpt-6-1-sol-vs-claude-opus-5-5-benchmark-reality-check) (2026-10-06): Vendor benchmarks for GPT-6.1 Sol and Claude Opus 5.5 barely overlap. Here's what independent evals, aggregators, and drift trackers show.
- [CLM vs Jev: The System 1 Decision Model Comparison](https://goremy.ai/blog/articles/clm-vs-jev-system-1-decision-models-compared) (2026-10-04): Jev made fast, cheap decision models mainstream. CLM makes them self-hostable and fine-tunable. A numbers-first comparison of speed, cost, accuracy, and ownership.
- [What Is RLCD AI? The Decision Architecture Behind Jev](https://goremy.ai/blog/articles/what-is-rlcd-ai-the-decision-architecture-behind-jev) (2026-09-28): RLCD trains models like Jev to output calibrated decisions, not text. Here's how it works, the real speed and cost numbers, and why 'can't hallucinate' isn't 'can't be wrong.'
- [GPT-6 Sol vs. Claude Opus 5.5 for Coding: What Changed for Internal Builders](https://goremy.ai/blog/articles/gpt-6-sol-vs-claude-opus-5-5-for-coding-the-real-cost-math) (2026-09-26): Claude Opus 5.5 wins on raw coding capability. GPT-6 Sol wins on cost per task. Here's what that means for building internal tools instead of buying SaaS.
- [What Are Jev-Class Models? The Fast, Cheap Alternative to LLMs for Internal Tools](https://goremy.ai/blog/articles/what-are-jev-models-the-fast-cheap-llm-alternative) (2026-09-26): Jev-class decision models trade text generation for typed choices, scores, and probabilities, running 40-400x cheaper and up to 200x faster than chat LLMs.
- [System 1 AI Models vs. JEV: What's the Difference?](https://goremy.ai/blog/articles/system-1-ai-model-vs-jev-the-difference-explained) (2026-09-24): What is a System 1 AI model versus JEV? A grounded look at fast, calibrated classifiers versus reasoning LLMs for GUI and form-filling agents.
- [What Is an Infinite-Parameter LLM, and What Does It Change for Data Engineering?](https://goremy.ai/blog/articles/what-is-an-infinite-parameter-llm-a-plain-english-guide) (2026-09-19): Infinite-parameter LLMs turn live data into model weights instead of prompts. Here's how they work, and what they change for data engineering teams.
- [GPT-6 Astra vs Fable 5.1: Which Should Build Your Internal Tools?](https://goremy.ai/blog/articles/gpt-6-astra-vs-fable-5-1-which-builds-better-internal-tools) (2026-09-12): GPT-6 Astra ties Fable 5.1 on coding benchmarks at a fraction of the cost, but Fable 5.1 wins on cache economics and long runs. Here's how to pick.
- [GPT-6 Astra vs. Local Models: The Code Privacy Math](https://goremy.ai/blog/articles/gpt-6-astra-vs-local-models-code-privacy-showdown) (2026-09-12): GPT-6 Astra costs 2.5x more than its predecessor and still isn't private by default. Here's the real cost and privacy math against local coding models.
- [GPT-6 Astra vs. Fable 5.1 for AI Agents: Which Model Wins?](https://goremy.ai/blog/articles/gpt-6-astra-vs-fable-5-1-for-ai-agents-2026-guide) (2026-09-10): Same list price, different bills. Compare GPT-6 Astra and Claude Fable 5.1 on cost-per-task, cache pricing, and benchmarks for internal AI agents.
- [What Are Looped Transformers in AI?](https://goremy.ai/blog/articles/what-are-looped-transformers-in-ai-explained) (2026-09-08): Looped transformers reuse the same layers repeatedly to reason in hidden math, not words. Here's how they work, why OpenAI's Astra uses them, and the safety tradeoff.
- [What Is Recurrent Architecture AI? GPT-6 Astra's Looping Explained](https://goremy.ai/blog/articles/what-is-recurrent-architecture-ai-gpt-6-astra-explained) (2026-09-07): Recurrent depth loops shared transformer layers instead of stacking new ones. Here's how it powers GPT-6 Astra and why it raises governance concerns.
- [How Do LLMs Actually Work? Beyond 'Next-Token Predictor'](https://goremy.ai/blog/articles/how-do-llms-actually-work-beyond-next-token-predictor) (2026-09-05): LLMs predict the next word, but that training objective builds real internal reasoning. Here's the proof, including a 13M-line Lean proof of Fermat's Last Theorem.
- [Claude Fable 5.1 vs OpenAI Astra: Which Should You Build On?](https://goremy.ai/blog/articles/claude-fable-5-1-vs-openai-astra-build-now-or-wait) (2026-09-04): Fable 5.1 is shipped, priced, and API-ready for internal agents. Astra has no release date. Here's what that means for teams building agent workflows now.
- [What Is AI World Modeling?](https://goremy.ai/blog/articles/what-is-ai-world-modeling-a-plain-explainer) (2026-09-03): AI world modeling means predicting the next state of an environment, not just the next word. Here's what it is, where it came from, and why it matters for coding agents.
- [Nvidia's Hugging Face Deal: What It Means for Open-Source AI](https://goremy.ai/blog/articles/nvidia-hugging-face-deal-what-it-means-for-open-source-ai) (2026-09-03): Nvidia's reported $12.9B bid for Hugging Face would put open-source AI's biggest hub under one chipmaker's control. Here's the impact and why owning your infrastructure matters.
- [GLM-5.3-Flash vs Qwen3.8-Flash: Which One Should Run Your Internal Tools](https://goremy.ai/blog/articles/glm-5-3-flash-vs-qwen3-8-flash-which-wins-for-internal-tools) (2026-09-03): GLM-5.3-Flash and Qwen3.8-Flash both collapsed AI pricing to $0.15/M tokens. Here's the real comparison and which SaaS subscriptions it makes obsolete.
- [The C&D Epidemic: Why Renting Platform Access Is a Business Liability](https://goremy.ai/blog/articles/c-d-epidemic-renting-platform-apis-is-a-liability) (2026-08-30): X's takedown of Nitter and XCancel is the latest proof that building on rented API access leaves your business exposed to a single letter or price hike.
- [Building Internal Multimodal Tools: GPT-5.6 Sol vs Gemini 3.7 Flash for Developers](https://goremy.ai/blog/articles/gpt-5-6-sol-vs-gemini-3-7-flash-for-developers-2026) (2026-08-29): A practical comparison of GPT-5.6 Sol and Gemini 3.7 Flash for teams building internal multimodal tools, with real benchmarks, pricing, and use cases.
- [Ignore the Benchmarkpocalypse: How to Choose an AI Model for Internal Tools](https://goremy.ai/blog/articles/how-to-choose-an-ai-model-for-internal-tools-not-benchmarks) (2026-08-29): AI benchmarks are broken. Here's how to actually choose an AI model for internal tools: test your own tasks, weigh cost and team comfort, skip the leaderboard.
- [De-Buzzfeeding the AI: Testing Open-Source Tone Wrappers on Claude and ChatGPT](https://goremy.ai/blog/articles/how-to-fix-claude-and-chatgpt-writing-style-tone-wrappers-tested) (2026-08-26): We tested open-source system prompts and skills that force Claude and ChatGPT to write like a professional, not a content mill. Here's what worked.
- [The Zero-Marginal Cost AI Era Is Breaking SaaS Economics](https://goremy.ai/blog/articles/why-is-b2b-saas-so-expensive-compared-to-ai-apis) (2026-08-25): AI inference costs fell 100x in two years, yet SaaS margins stayed near 80%. Here is why the subscription price never followed the token price down.