Artificial intelligence

Artificial intelligence

In internal tests across reasoning, coding, and writing, Avocado lags behind the top models from Google, OpenAI, and Anthropic. The model performs better than Meta's previous model, Llama 4, which failed to meet expectations last year. It does perform better than Google's Gemini 2.5 from March 2025. However, Gemini 3.0 from November is still a step too far.

When the user asks "What enemy military unit is in the region?" the AIP Assistant guesses that it's "likely an armor attack battalion based on the pattern of the equipment." This prompts the analyst to request a MQ-9 Reaper drone to survey the scene. They then ask the AIP Assistant to "generate 3 courses of action to target this enemy equipment," and within moments, the assistant suggests attacking the unit with either an "air asset," a "long range artillery," or a "tactical team."

Right off the bat, Claude had fewer bugs generating code than ChatGPT. We didn't have any major frustrations with ChatGPT that forced us to switch, but I think one of the promises of AI is to speed up our work, and Claude let us move faster because we spent less time correcting it.

Google AI mode has added an 'Ask about this' option above the sources where all URLs are displayed. Clicking on 'Ask about' here automatically pulled a new prompt into the search box.

Simply having data isn't enough-how you organize, govern, and activate it makes all the difference. Leading organizations implement three specific practices: connect all your data together, label and organize it so it's easy to find, and set controls to ensure only the right people (or agents) have access to sensitive data sets.

AI depends on five layers-energy, chips, infrastructure, models, and applications-and that all five need to scale together to enable the massive buildout of AI across the economy. Nvidia, not coincidentally, sits squarely in the middle of that stack, and connects most of the layers together.

Our war fighters are leveraging a variety of advanced AI tools. These systems help us sift through vast amounts of data in seconds so our leaders can cut through the noise and make smarter decisions faster than the enemy can react. Humans will always make final decisions on what to shoot and what not to shoot and when to shoot, but advanced AI tools can turn processes that used to take hours and sometimes even days into seconds.

Four generations, MTIA 300, 400, 450, and 500, have been produced within less than two years, with several already in production and others scheduled for mass deployment in 2026 and 2027. The quick pace is deliberate. Rather than betting on a single chip generation and waiting years for results, Meta has adopted a roughly six-month cadence per generation, using modular chiplet architecture to enable incremental upgrades without replacing entire rack systems.

Meta is building these chips because buying AI hardware at scale is expensive, and relying too heavily on external suppliers leaves less room to shape that hardware to its own needs. Building more in-house could help the company keep AI costs in check.

A financial analyst can pull comparable company financials from an open workbook and other data sources. From there they could build out a trading comps table in Excel, drop the valuation summary into the pitch deck, and draft the email to the MD - without switching tabs or re-explaining the dataset at each step.

According to the new post, AI credits can now be used for Antigravity, with subscriptions providing some built-in credits while further credits are available for purchase as needed, at a cost of $25 for 2,500. Exactly what a credit is worth when used with Antigravity is not described in the documentation.

"My tiny little noodles, my sweet girls, don't look down," a momma pasta sobs in one clip on TikTok. "Mommy, the air is burning, I'm scared," one of the baby bundles cries. "I don't wanna fall." The spaghetti is then submerged, graphically, into the boiling water, before being dressed up in red sauce.

There's a big bet that the quality of software that gets produced is going to improve exponentially. If that doesn't happen, that's a big threat. Many AI coding tools can generate apps quickly, but the output can still be buggy, fragile, or difficult to scale. The vibe coding industry depends on those systems getting better.

Marvell reported Q4 FY2026 revenue of $2.22 billion, beating Wall Street expectations near $2.21 billion. Non-GAAP EPS came in at $0.80 versus the $0.79 consensus estimate. Those are modest beats on the headline numbers, but Cramer's "$1 billion more in sales than anyone thought" framing appears to reference the magnitude of data center revenue relative to where expectations stood earlier in the year.

Nvidia posted $68.1 billion in revenue, up 73% year over year, with Data Center Networking revenue surging 263% to $10.98 billion, driven by NVLink infrastructure scaling across hyperscalers. EPS came in at $1.62 against a consensus estimate of $1.52. Full-year free cash flow hit $96.6 billion.

DescrybeLM answered all 200 correctly. The general-purpose models each missed between 13 and 23 questions, achieving accuracy rates ranging from 88.5% to 93.5%. Rubric-scored reasoning quality - a separate measure evaluating whether systems correctly identified governing legal rules and applied them to the facts - followed a similar pattern. DescrybeLM scored 99.70% on that dimension.