BILLIONAIRE MARKET LEGEND JEREMY GRANTHAM JUST WARNED ON CNBC #BITCOIN# IS "USELESS, SPECULATIVE INVESTMENT" "IT WILL WITHER AWAY WITH A WHIMPER OVER YEARS AND DECADES" "IT IS NOT A STORE VALUE. IT JUST HALVED IN A STRONG ECONOMY. IT IS DIFFICULT TO USE TO MAKE SERIOUS TRADES." "WHAT THE HELL DOES IT DO? IT ALLOWS CROOKS TO MOVE MONEY WITHOUT A TRACE" 👀

Arknights: Endfield EP - Si [The Hour of Floral Rites] Butterflies flit into the painted world, where flowers bloom in every season. All things wither and bloom again, like butterflies emerging from cocoons. #ArknightsEndfield# #Endfield# #SanctuaryOfInk#

“Over time, we in the West came to feel ashamed of the very things that made us exceptional” “In the process we opened our borders, and outsourced our industry, and allowed our militaries to wither and to decay.” “…we seemed to outsource more of our sovereignty, transferring our decisions to global bureaucracies far beyond the reach of our control.” “We will either choose to act now, or lose the choice to act at all.” “…there was nothing selfish or wrong about loyalty to your nation, nor anything selfish or wrong about fighting to defend its interests above all else.” “The story of our civilization is the greatest story ever known to man”

SWE-Together Update: Claude Fable 5 and 5.1 are the new top models on SWE-Together, and Meta's Muse Spark 1.3 is the best value on the board. SWE-Together is our benchmark of 109 real coding tasks with a simulated user in the loop, built to tell you whether a new model will actually match your expectations before you hand it your work. Six quick findings from the updated board: 1. Fable 5 beats 5.1 on consistency, but 5.1 is more efficient. At their best the two actually perform the same. The gap is in the worst runs, where Fable 5.1 scored zero on 14 trials and Fable 5 on only 6. However, 5.1 is 25% faster, about 2x cheaper, and needs slightly fewer corrections from the user (1.45 vs 1.53 corrective messages per task) than Fable 5. 2. Muse Spark 1.3 is the value outlier. Per solved task, it is about 2.5x cheaper than Fable 5.1 and 5x cheaper than Fable 5. Performance-wise, it ties with Fable 5 and 5.1 for best on Python (73%) and Rust (100%). 3. Newer is not automatically better at collaborative coding. GPT-5.6-Sol is the newer model, yet it needs more corrections from the user than GPT-5.5 (1.66 vs 1.59 per task). 4. "Stronger models need less steering" is a trend, but not guaranteed. With more models added, the correlation between pass@1 and user correction weakens from -0.92 to -0.72, and Fable 5 takes more corrections than Opus 4.8 (1.53 vs 1.38) despite a 6 pp higher pass@1. 5. Frontier progress contributes greatly to stability. Fable 5 converts 89% of its pass@1 into pass² (both runs solve), versus 60 to 67% for DeepSeek V4 Pro, GLM-5.1, and MiniMax 2.7. 6. There is still plenty of headroom. 16 of 109 tasks have zero solves across all tested models, and even Fable 5 at 69% pass@1 sits about 9 pp below the ~78% that the original human patches scored. The full leaderboard, with per-task and per-trial breakdowns, is at and the benchmark is open source at

Practices for embedding AI agents into enterprise systems [Async Job + Load Control] 💡 "Everything real-time" is a path to collapse. Turning long-running tasks into background jobs with priority queues gives you an agent platform that survives traffic spikes. 🔥 Problems solved - HTTP timeouts on agent tasks running tens of seconds to minutes - Spike traffic degrades latency for all users, causing cascading failures - Low-priority batch work starves real-time conversations of resources - Uncontrolled LLM call costs during traffic surges 🏗️ Proposed pattern Return a job ID immediately upon request and enqueue the task to a background queue. Stream progress via SSE/WebSocket and deliver results through webhooks or Slack callbacks. Use priority queues to ensure real-time conversations always come first while background analytics are deferred. Separate "online intelligence" (lightweight models for instant responses) from "offline batch intelligence" (heavy models running overnight), then let daytime agents reference precomputed results for instant answers. ✅ Selection criteria - Use when: Tasks exceed tens of seconds, high-volume parallel processing, spike-prone multi-tenant environments - Skip when: Conversational interactions completing in seconds (job overhead hurts UX) ⚠️ Pitfalls - Without a Dead Letter Queue, failed jobs silently disappear - Stale offline batch results can lead to wrong decisions if freshness is not managed - Missing per-tenant quotas let one runaway tenant degrade the entire platform 🛠️ Implementation Approach 1. Set up a message queue (SQS / RabbitMQ / Kafka) to accept jobs and return a job ID immediately via API 2. Use a workflow engine (Temporal / AWS Step Functions) to centralize job progress tracking, retries, and DLQ handling 3. Implement priority queues to separate real-time conversations from background work, with per-tenant quotas (Token Bucket / Sliding Window) 4. Stream progress updates via SSE/WebSocket and deliver results through webhooks or Slack callbacks on completion 5. Run heavy analytics overnight via batch pipelines (Airflow, etc.) using large models, storing results in Redis/DynamoDB for instant retrieval by daytime online agents #AIAgents# #EnterpriseArchitecture#

💥 GLM-5.3-Flash: Reported Opus 4.8-Level Scores at One-Tenth the Price Zhipu has confirmed that Ox Alpha — the anonymous model that dominated OpenRouter and OpenCode last week — is GLM-5.3-Flash, a 320B-parameter MoE with 18B active, released with open weights and an API at roughly one-tenth of GLM-5.3's price. Zhihu contributor 小小将 opens with a confession: he had hoped Ox Alpha was an external team fine-tuning the open GLM weights. He was wrong — it was Zhipu's own model all along. His broader take: at this performance and this price, Flash becomes the new "kill line" for large models — the bar below which rivals simply get priced out. 1️⃣ The numbers behind the "kill line" Flash is somewhat larger than DeepSeek-V4 Flash, at 320B total and 18B active. On Zhipu's own benchmark comparisons, overall capability is roughly level with Claude Opus 4.8. More reference points he cites: 🔹 57 on the Artificial Analysis Intelligence Index — around GPT-5.6 Terra's level, slightly below GLM-5.3. 🔹 No.5 on Code Arena, one spot above GLM-5.3. 🔹 API at 1/10 of GLM-5.3's price, with a limited-time half-off promo bringing it to 1/20. 2️⃣ Same DeepSWE score, a fraction of the cost The author's sharpest comparison is on DeepSWE, where Flash scores 63% at a single-task cost of $0.24. DeepSeek-V4-Pro hits the same score at $1.67 per task — roughly seven times the cost for equivalent results. This is the author's cost arithmetic on reported figures, not an independent measurement. 3️⃣ The architecture that cuts the bill Part of the price drop is structural. Flash uses a hybrid of linear and sparse attention, sharply cutting attention compute, and a new IndexPool that compresses the indexer's cache from four copies to one — reducing latency and memory at million-token context. Versus GLM-5.3, the company reports attention compute down to about 1/3 and KV cache down to about 1/4.4. 4️⃣ The compute mystery, answered by domestic chips One reason few believed Ox Alpha was Zhipu's: the company was not thought to have enough spare compute for a massive free public test. The answer, per Zhipu: Flash is served from domestic Chinese chip clusters. The team built a custom inference engine on SGLang and worked around limited VRAM and bandwidth with quantization, layered deployment, and trading compute for communication — lifting end-to-end serving performance about 3x on the same hardware, with per-token cost now close to mainstream NVIDIA GPUs. 5️⃣ Chinese silicon just passed its biggest stress test The author's conclusion: Chinese chip clusters have shown they can carry large-scale inference for a frontier-level model — including a free, record-breaking public trial. If that holds, he argues, the outlook for Chinese models just got a lot brighter. 🔗 Full Reading: #GLM# #Zhipu# #GLM5Flash# #AIChips# #LLM# #AIInfra# #OpenWeights#

🚨SlowMist TI Alert🚨 💸 @usmfum Loss: ~70.83 ETH 🔍 Root Cause: Pricing logic flaw in `ethFromDefund()` of USM's `defund()` function. The function uses arithmetic mean of current and estimated final FUM sell prices for single redemptions, but lacks "split invariance." Combined with per-redemption state contraction (`adjShrinkFactor`) and integer rounding, 64 small `defund()` calls return more ETH than one large call for the same FUM amount. - Attacker EOA: `0xb92b2e47680c89da8f951b8963ef469f461a50fc` - Attacker Contract: `0x5a5e29ba89663a3558273354e990426f3cac7de7` - Victim Contract (USM): `0x2a7fff44c19f39468064ab5e5c304de01d591675` - Profit Receiver: `0xe3c6346b6f282029312d2caf4677ef39beabbf99` Powered by Tx:

Colman’s wasabi-style punch made the mustard a British institution. Now the farmers who protect its seeds hope new American owners don't let the brand wither. Read more: 📷️: Getty Images