Tired of winning

@maddenifico: Trump's Beef Catastrophe: Incompetence, Blithering Idiocy, and Blatant Lies Have Obliterated America's Beef Industry

In yet another humiliating display of incompetence, the authoritarian Trump regime has turned America's once-mighty beef industry into a national punchline. After months of touting "record" export numbers as proof that his chaotic trade policies were somehow working, the U.S. Department of Agriculture has been forced to admit it inflated beef export sales by a staggering 90 percent.

According to a bombshell Reuters report, the USDA bragged in early July that U.S. exporters had sold more than 126,000 metric tons of beef to foreign buyers for a sales period ending in late June. The actual number? Just 12,000 metric tons. That's not a rounding error. That's a catastrophic fabrication that would embarrass a high-school bookkeeper.

The fake numbers were so absurd they should have raised red flags immediately. The USDA claimed record sales of more than 38,000 metric tons to Chile and over 32,000 metric tons to Italy — countries that aren’t even major markets for American beef. Reality? A pathetic 367 tons to Chile and 350 tons to Italy. Fourteen other countries saw their numbers slashed as well.

This isn't just bureaucratic sloppiness. This is the Trump regime's signature brand of hype, lies, spin, and propaganda colliding with economic reality — and the American cattle industry is paying the price.

U.S. beef prices have hit record highs this year thanks to tight cattle supplies and strong domestic demand. Exports have been declining since 2022. Higher prices have priced American beef out of the global market, as commodity analyst Austin Schroeder bluntly told Reuters: "We're priced out of the world market to a certain extent. It wouldn't make a lot of sense for that big of an export number."

Meanwhile, domestic supplies are so low the U.S. has had to ramp up beef imports. Earlier this year, a screwworm outbreak forced Texas to declare a state of disaster, further hammering producers already squeezed by Trump-era chaos.

Instead of acknowledging the mess his administration created — from trade wars that disrupted markets to regulatory neglect that left ranchers vulnerable — Trump and his sycophants simply let the USDA pump out fantasy numbers and call it a win.

This is the same pattern we've seen across the board: brag about successes that don't exist, then quietly revise the data when caught. Only this time it's not some abstract economic indicator. It's the backbone of rural America — cattle ranchers, feedlot operators, processors, and exporters — watching their industry get trashed by incompetence and covered up with lies.

The revised numbers aren't just embarrassing, they're devastating. They confirm what ranchers have been screaming for months: America's beef sector is in serious trouble, and the people running the show are too busy tweeting victory to notice or care.

The only question left is how many more "record" numbers will have to be walked back due to Trump being a serial lying sociopath who is too psychologically fragile to handle the truth.

GPT-5.6 Sol is available as an orchestrator model inside Computer. Both Sol and Terra are also available as models for search for Pro and Max users! Enjoy!

@perplexity_ai: GPT-5.6 Terra and Sol are now available in Perplexity and Perplexity Computer. https://t.co/TeIyQGfCRP

Top 10 AI Loops You Must Build

it's possible that when a fully local harness + model + runtime gets good enough, it becomes the best gateway for you to use the frontier models too, which end up becoming advisor tools inside that harness; the harness then ends up turning into an OS for using frontier tokens.

Very cool to see @netflix releasing video datasets and models on @huggingface. They have a strong video AI team and would be amazing to see them open-source more!

https://t.co/5bVKjn6wEp https://t.co/V0QTnVDNNj

GPT 5.6 vs Claude Fable 5: The Best AI Model Across 6 Practical Use Cases

Adam Thierer had written the landmark book on "Permissionless Innovation." We get the best ideas when we don't have to ask the government in advance for permission to invent. Protecting open source AI is now a critical part of ensuring this.

@AdamThierer: We stand at a critical crossroads in the debate over AI governance in the United States, and it feels like we are inching closer to a very serious battle over whether or not open source models will even be allowed in an environment where a new de facto licensing regime has been taking shape.

Lacking formal congressional statutory frameworks or clear administration rules (like the diffusion rule revision), we appear to be left with a sporadic, arbitrary, non-transparent process for model review. The fiction of “voluntary” agreements hangs over this debate, and some large model developers are already showing an incredible willingness to bend over backwards to accommodate national security-related officials / orders that the rest of us are not privy to. It's a very opaque process. And those model developers are expected to play ball with those officials, or else their models get pulled from the market or held up for long periods. Or they will lose any government procurement contracts they have. There is nothing “voluntary” about it when that Sword of Damocles hangs in the room.

As this mess worsens, at some point the question of how to handle open source models will come into sharper focus because it will have to. I've even heard some rumors lately that something may be coming from the admin on this front to address this.

Needless to say, if this informal new AI model review regime expands and takes on more pre-vetting characteristics / requirements, it is hard to see how open source players could comply with such quasi-licensing of AI models. Specifically, if this ambiguous new regime is accompanied by a general presumption of ‘restrict-until-permitted,’ then that would spell doom for open source. That is a very dark path for our country.

Worse yet, of course, would be a move by national security officials to more directly restrict open source models and capabilities. If that happens, then we would be right back in the thick of a Clipper Chip-like battle along the lines of what we saw in the late 1990s. That is a much darker path for America.

Meanwhile, open source developers have no “golden shares” or other goodies to offer the government to make their problems go away.

Let’s be clear: If our government takes the dark path, it will become the single most important battle over computational freedom of modern times. It is time for people to make a stand in defense of open source before it is too late.

The enormous progress in enterprise-useful AI – across multiple industry domains – is being driven by large investments in industry-specific training and evaluation data.

Only bits of that data are openly available. Great to see this higher quality open finance benchmark from @samaya_AI!

@maithra_raghu: Excited to be releasing FrontierFinance, the largest and most challenging open benchmark for evaluating AI agents across the full investment workflow!

FrontierFinance is substantially harder than current finance benchmarks: Existing benchmarks like FinanceBench and Finance Agent focus almost entirely on data extraction.

FrontierFinance spans diverse use cases across the full investment process: Screening & Discovery, Company Research, Sector/Industry/Macro, Earnings & Events, and Coverage & Catalyst Monitoring.

Created for ambiguous, long-horizon agents: 220 examples paired with 11,543 expert-crafted rubrics, following Samaya's Criteria Eval methodology. The rubrics are what let us evaluate the reasoning and steps behind a true expert-level output, not just a plausible-looking one.

Evaluations: We evaluated Claude Fable 5, Claude Opus 4.8, GPT 5.5, Gemini, open-source models including GLM and DeepSeek, and others. We used the same public rubric and a standard harness for financial tasks. Samaya's AI system reached state-of-the-art accuracy at 50.8%, at 4x lower inference cost than Fable 5. Next best was Fable 5 (49.2%), then Opus 4.8 (45%) and GPT 5.5 (43.5%).

We're releasing the benchmark, methodology, and full evaluation results - see link in comments.

Future releases: FrontierFinance was curated from Samaya's larger internal set of ~5,000 examples, and we plan to release subsequent, harder benchmarks as well as a more detailed technical report!

this is slowly but surely happening

@AravSrinivas: Every enterprise will have its own model-harness-sandbox-eval flywheel with token value per watt optimization. This is the future. Simple reason: tacit knowledge about the domain and customers and their workflows that the company uniquely understands and has built trust around.

We are hosting this model in B200s in the US, and will next run a post train of Nemotron 3 Ultra, to get the same capabilities out of American open source models. More soon!

Long term, we think the right way to orchestrate open source and frontier models is to post-train them to get good at advisor escalation to a frontier expensive model when necessary, inside an agent harness that’s already deployed in production to millions of users. Perplexity is uniquely positioned to solve this problem.

We’ve been post-training a version of GLM that is trained to escalate to a frontier model inside the Computer harness. When paired with an advisor, this model functions at Opus 4.8 grade performance at a fraction of the cost. Available now as a research preview!

@perplexity_ai: We're releasing a research preview of a new orchestrator model in Perplexity Computer.

The model is an adapted version of GLM 5.2, post-trained for the Computer harness. It delivers near-frontier performance at 0.344x of the cost of Opus. https://t.co/jcxikoFRfn