RT @jowettbrendan: R.I.P Gartner. You don’t need expensive analyst subscriptions anymore. You can now generate full industry reports usin….

I hope you've found this thread helpful. Follow me @jowettbrendan for more. Like/Repost the quote below if you can:.

If you’re serious about building with AI (not just testing tools). I’d love to have you in my free community. It’s where I drop agent templates, walkthroughs, and ideas that don’t fit in a post. Check it here here:

Most people ask LLMs to “summarize an industry.”. That gets you fluff. But assign the role of an analyst, define the structure, and set expectations?. You get Gartner-level clarity for free.

Want forecasts for:. • Generative AI?.• Fintech apps?.• Edtech in Asia?.• Climate tech startups?. Paste the prompt. Ask a specific question. Review the 5-part output. Iterate. That’s it.

Gartner charges thousands per year. This costs you nothing but a good prompt and a few smart follow-up questions. No subscriptions. No analyst access. No paywalls. Just better prompting.

Grok 4 output:

I tested this on 3 real-world topics:. • AI note-taking apps.• LLM ops platforms.• Wearable health tech. Each LLM produced a full 5-part breakdown with vendor maps, projections, and risks in 30 seconds. Here’s what came out:.

Here's the mega prompt that turn Grok 4 to Gartner:. "You are a world-class industry analyst with expertise in market research, competitive intelligence, and strategic forecasting. Your goal is to simulate a Gartner-style report using public data, historical trends, and logical.

First, let’s see what Gartner actually does well:. 1. Structured industry forecasts.2. Competitive landscape mapping (e.g., Magic Quadrants).3. Strategic insights for enterprise buyers.4. Vendor comparisons with pros/cons.5. Trend analysis backed by years of data. These are.

R.I.P Gartner. You don’t need expensive analyst subscriptions anymore. You can now generate full industry reports using Grok 4 and public data. Here’s the prompt that turns Grok 4 into a full-stack market research analyst:.

RT @jowettbrendan: NVIDIA just released OpenReasoning-Nemotron. And it outperforms models 10x its size. The 32B model rivals GPT-4 on m….

NVIDIA’s OpenReasoning-Nemotron is a gift to the open-source AI community. If you’re working on agents, solvers, or reasoning tasks start here. Follow me at @jowettbrendan for more breakdowns of breakthrough AI models. Like, repost, and bookmark this for later.

If you’re serious about building with AI (not just testing tools). I’d love to have you in my free community. It’s where I drop agent templates, walkthroughs, and ideas that don’t fit in a post. Check it here here:

You can download the models today on HuggingFace:. → 1.5B.→ 7B.→ 14B.→ 32B. Start tinkering. Benchmark them. Fine-tune. Push the edge.

Built with NeMo-Skills:. → Data generation.→ Distillation.→ Training.→ Evaluation. The entire stack is reproducible. The dataset? Coming soon. The code? Already open.

All of this was done without RLHF. OpenReasoning-Nemotron is trained only with SFT using distilled data. This shows just how far we can go with massive, domain-specific datasets. Now:. → You can start reasoning-based RL research from a near-SOTA baseline.→ Customization for.

But here's the twist:. They added “GenSelect” inference a multi-agent voting mechanism. Using this, the 32B model achieved:. → AIME25: 93.3.→ HMMT Feb 25: 96.7.→ LiveCodeBench: 75.3. This rivals OpenAI’s o3-high tier. Let that sink in.

Benchmark highlights (pass@1):. 7B:. → AIME25: 78.2.→ GPQA: 61.1.→ MMLU-Pro: 71.9. 32B:. → AIME25: 84.0.→ HMMT Feb 25: 73.8.→ LiveCodeBench: 70.2. These are SOTA scores for models in their size class.

The result? OpenReasoning-Nemotron. A family of 4 open models (1.5B, 7B, 14B, 32B) that dominate on math, science, and code benchmarks. No Reinforcement Learning. Just elite distillation using NeMo-Skills.