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iDoMnCi
@DomAtSiteSage
trying to understand our place here one byte at a time data & ai @redhat building SiteSage w/ lovely @AmberAtSiteSage
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So what is this? A MCP? Exclusively supports only claude? New product from SF? This turned me away because it was all around claude Good thing we’ve been building variations of this internally. Helps when all data across the org is available from snowflake.
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Welcome Claudeforce. ❤️
Hermes real-profile browsing is a copy of my active Chrome logins. Not my live window. Off by default. Hermes snapshots cookies and logins, then drives its own Chromium. Settings, Browser, Use My Real Browser Profile. Or set browser.use_real_profile true. I would leave it off until the agent needs a site I am already signed into. It does not solve captchas. Cloud browsers still own that. Turn it off and the snapshot is deleted on the next browser use.
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Hermes Agent can now seamlessly browse as you. Turn on real-profile browsing and your agent acts with your logins, from a managed copy of your existing Chrome profile.
me and my main agent watching a subagent do all the work
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🚨 This is a BIG upgrade for anyone using Hermes for data work. Hermes can now run execute_code with a SESSION-PERSISTENT Python kernel. 🔥 Load a 2GB dataset once. Build your DataFrames. Run analysis. Ask another question. Keep going. Hermes doesn’t have to rebuild the entire Python environment every damn call anymore. That’s a proper agent workspace. 🤘
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Everyone’s passed out as I wrap up another day at the prompt factory Lua looks comfy 🥱
Most agent memory benches score recall. StateMemBench scores whether the answer uses the current fact or a superseded one. 234 multi-session scenarios. 322 graded probes. Closed-pool grading for state drift. StateMem lifts current-state accuracy about 1.8x over the strongest same-backbone memory baseline on DeepSeek-V4-Flash (0.199 to 0.363).
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We gave our AI system a spec, and in under 2 weeks, it designed, verified, and deployed a chip that beats NVIDIA. It’s built for low-power physical AI workloads. We’re running live inference on >B+ parameter models like Llama, Qwen, and Kimi, serving at 3.4x better perf/watt than NVIDIA Jetson. From just a specification, our AI system autonomously generated all of the RTL Design, UVM verification, formal proofs, firmware, drivers, and kernels, co-designing the model, software and silicon as one optimization loop. Better AI can now design better chips to run AI, leading to a loop of recursive-self improvement towards our path to abundant intelligence.
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While America begins to rightfully castrate Canada economically, please consider the Americans who live here. Many of us have been here for years, long before the tensions between our governments. The people will suffer through this. Penance for the way the majority of us vote. ✌🏻
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So @cursor_ai, the additional token rate charge of $0.25 per 1 million tokens needs to be scrapped. I have to pivot to new tools because it’s clear an effort is underway to phase out Cursor. Not happy about it. This single CTR charge is so damaging. Make your money on VTDs, not up-charging clients.
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Harness optimization is getting real receipts. AutoSaddler is offline harness learning from agent failure traces. Not another prompt tweak loop. It patches prompts, tools, and middleware as code. Then it keeps updates that survive a held out set. On the test sets (Pass@1): GAIA2: 53.0 → 62.0 (+9.0) SWE-Bench Pro: 37.3 → 46.9 (+9.6) Terminal-Bench 2.0: 40.0 → 50.0 (+10.0) That TB2 number also clears the expert tuned Terminus KIRA at 47.5. Kill generalization aware selection and GAIA2 falls to 50.6, under the default agent. Deep diagnosis and structured patches help. Dev set filtering is what stops the harness from overfitting the mini batch. On GAIA2, Figure 1b, about 147 leveraged traces to the best dev score vs about 1,400 for Meta-Harness.
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When is cursor agent going to be able to steer, interrupt, create, archive and delete other agent sessions? Please. I’m freaking begging you. Add these tools 👉 @poteto 🫪 @ericzakariasson 👈
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Diving into this ASAP
Introducing Headlong, an open source microharness for persistent agents: self-guided agents that think continuously. Most agent harnesses are reactive: you send a task, the agent completes it, and then it sits frozen until the next request. Cron jobs and heartbeats wake it up to run a checklist and put it back to sleep. A Headlong agent is never asleep. It keeps generating thoughts about whatever it decides is interesting, in a self-guided loop inspired by human inner monologue. Your message doesn't start a session. It's one more observation that lands in the agent's thought stream, and the agent decides if and when to reply. Headlong is built on the idea of persistent agency: continuous inner thought generation between external interactions. The agent sets its own interests and priorities, comes up with its own projects, and sometimes pings you unprompted with progress. To keep our prototype as simple and small as possible, we implemented Headlong as a microharness: a complete agent harness in under 10K lines of Bash, organized as a handful of small executables. It includes a loop that generates the next thought, shellm (a recursive language model written in Bash), a trajectory stored as a DAG of jsonl files, and context as a projection of that trajectory. We've been running one Headlong agent internally at Laude for several weeks. The whole team talks to it over Slack and Telegram, and every conversation lands in its single stream of thought. It works in its own fork of Headlong and we've pulled over 50 of its commits into main. One night, with nobody talking to it, it went back to check whether a recall process it had built was actually wired into its mind, found that it wasn't, diagnosed and fixed the bug, and verified the fix end to end. 48 minutes, no human asked for the fix or was in the loop at any point. Every step is a timestamped line in its log. Things broke too, and we wrote those up. Background thinking costs us $1 to $2 an hour, our agent stopped its own service three times by accident, and self-delegation died on day one. Details in the post. One line installs everything and starts an agent. Use a dedicated sandbox and spend-capped API key; it runs real shell commands and thinks around the clock. Headlong is research software, be careful! curl -fsSL | bash Launch post: Repo: Headlong is a @LaudeInstitute / MIT collaboration.
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I’m really excited for grok 4.7
The problem with being ahead of the curve is, everyone thinks you're retarded.
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Fully remote workers report the highest well-being, and are less likely to quit, per FORTUNE
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People should take some personal accountability and stop using platforms that store emails, phone numbers, IPs, and device IDs in a form they can just hand over on demand. There are already messaging apps built so this exact scenario is impossible.. ones that use end-to-end encryption, zero-knowledge design, aggressive data minimization, and no long-term metadata logging. When those companies get a subpoena, the honest answer is “we have nothing useful to give you.”
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Update: They’re not just hunting the leaker. Take-Two got court approval to pull personal info (emails, phone numbers, IPs, device IDs) from ~100k Discord users across multiple servers... including DarkViperAU’s 90k-member server. Most of those people are complete bystanders who had nothing to do with the leaks. A multi-billion dollar company turning Discord into a mass surveillance tool the second their IP gets threatened. And remember… once they have that personal data, there’s no real guarantee they can even protect it. These big companies get breached regularly. When (not if) it happens again, your info is just sitting there waiting to be leaked. Greed + power = this exact playbook.
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This is a freaking awesome article. Well worth the read if your using agents and especially grok @bot I find it interesting that early on everyone shifted away from subagents because it was too much to manage. Now it’s the opposite because the agents manage each other.
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