Agent skills memory autonomy: Coordinating agents at scale

Agent skills, memory, autonomy

An autonomous “digital twin” is now running a Twitter account end-to-end, hinting at where agentic systems are headed outside the lab. Built on the DroidRun framework, “TweetFire” drives the X app like a human: logging in, scrolling feeds, reading, and posting context-aware replies on specific topics up to four times a day. Under the hood it tracks token usage and request patterns, and its builder frames it not as a scheduler, but a social “AI ops” bot for presence with context. The early community reaction mixes enthusiasm with the predictable worry that better bots accelerate the “dead internet” trend, where distinguishing humans from agents becomes difficult in mobile-first ecosystems (more: https://www.reddit.com/r/LocalLLaMA/comments/1on8c5b/i_used_llama_droidrun_to_create_a_selfrunning/).

Moving from social to skills, OpenSkills lets users import Claude Skills locally—no Anthropic dependency—and execute them in a Mac-only code container the author claims offers “better isolation than Docker.” Crucially, it works with any LLM that supports the Model Context Protocol (MCP), so PDFs, videos, and images can be processed without leaving the device. Linux support is “in the works,” and the project shows how the MCP ecosystem can decouple skills from specific front-ends while improving privacy by default (more: https://www.reddit.com/r/LocalLLaMA/comments/1ojdvg4/openskills_a_open_sourced_and_completely_private/).

A complementary thread is agent learning from experience—without fine-tuning. The Agentic Context Engine (ACE) wraps any LLM via LiteLLM and uses three roles—Generator, Reflector, Curator—to build an evolving “playbook” of strategies that help, harm, or are neutral, then re-injects that distilled context into future executions. Demonstrations include the “seahorse emoji challenge” where the agent learns there is no seahorse emoji after initially hallucinating one, and the project offers Opik integration for monitoring and train/test splits to avoid overfitting in benchmarks (more: https://github.com/kayba-ai/agentic-context-engine).

Finally, the agent memory debate is sharpening: thread-based buffers (simple rolling windows per conversation) versus session-based state that tools and multiple agents can share. The latter can reduce context window pressure by caching outside the prompt and avoid lossy summaries, at the cost of more tokens and system complexity—likely better for multi-agent setups, while threads remain the pragmatic choice for single-bot chat (more: https://www.reddit.com/r/ollama/comments/1omjjbv/thread_vs_session_based_shortterm_memory/).

Coordinating agents at scale

Even strong agents stumble when paired. A widely shared “collaboration gap” editorial highlights a maze task where two agents must agree on every move given partial maps. Performance drops sharply in pairs, with “infinite politeness loops” and grounding failures under partial observability. Ordering matters: letting the stronger model go first to set conventions improves outcomes. The proposed “relay inference” fix—one well-framed message from the stronger agent to seed collaboration—closes much of the gap. The broader point, quoting Grosz (1996), is that “capabilities needed for collaboration cannot be patched on.” Coordination is its own competency, not an automatic byproduct of better reasoning (more: https://www.linkedin.com/posts/stuart-winter-tear_the-agent-collaboration-gap-activity-7391806850708500480-m_rT/).

Demand for structured, multi-document workflows is growing in knowledge professions, notably law. A power-user lawyer in India describes using Claude, GPT-5, and Perplexity for drafting, issue spotting, chronology tables, and argument generation, but seeks systematic methods for version control, persistent context, and deep reasoning (e.g., anticipating counter-arguments). Replies point to agent pipelines with retrieval-augmented generation over case files and citations, built with vendor APIs behind a custom app—reflecting an emerging pattern: codify the workflow first, then orchestrate agents inside it (more: https://www.reddit.com/r/ClaudeAI/comments/1ojcg6p/looking_for_advanced_workflow_tips_how_are/).

On the deployment side, a venture firm aggregates “lessons from interviews on deploying AI Agents in production,” reflecting a founder-focused take on where agentic systems deliver ROI today. The research-led note underscores that getting from demo to durable value requires aligning agent capabilities with specific domain workflows—an echo of the collaboration and orchestration challenges above (more: https://mmc.vc/research/state-of-agentic-ai-founders-edition/).

Tooling, formats, and developer UX

Support for MiniMax M2 has landed in llama.cpp, but with a caveat: the model’s “interleaved” thinking format complicates chat templating. Maintainers are considering leaving tags inside normal content for clients to parse and adding tool-call parsing, and even pondering /messages-style APIs to handle reasoning blocks cleanly. Community observations note occasional missing opening tags in other runtimes and availability of an MXFP4 quant for testing—useful, but application developers should expect to handle reasoning tags explicitly for now (more: https://www.reddit.com/r/LocalLLaMA/comments/1ol6qlk/minimax_m2_llamacpp_support_merged/).

On prompt plumbing, Token-Oriented Object Notation (TOON) for Python targets 30–60% token savings over JSON by using indentation for nesting and CSV-like tables for uniform arrays, plus optional length markers for validation. A CLI handles encode/decode, with strict validation by default and a lenient mode available. The repo is deprecated in favor of the official implementation, but its documentation shows how structured, model-friendly formats can cut costs without losing semantics (more: https://github.com/xaviviro/python-toon).

For media workflows, “video-to-txt” is a multimodal pipeline that extracts keyframes, transcribes audio with Whisper, runs visual and multimodal analysis, then generates reports and summary media (video/GIF)—all wrapped in a WebUI with streaming. It supports both local Ollama and OpenAI-compatible APIs, Docker deployment, and includes prompts for frame analysis, summaries, and quality metrics. It’s a practical template for teams standardizing end-to-end video understanding (more: https://github.com/lzA6/video-to-txt).

Developers also continue to optimize their IDE+AI stack. Recommendations trend toward: VS Code with GitHub Copilot as the default value pick; Windsurf for a fixed budget; Cursor for codebase-aware “plan mode”; and Zed paired with Z.ai’s GLM 4.5 to save cost while keeping Claude Code-style workflows. A recurring tip is to decouple IDE from the AI engine—use CLI tools (e.g., opencode.ai) and BYOK setups to maintain flexibility and control spend (more: https://www.reddit.com/r/ChatGPTCoding/comments/1omdsep/which_ai_ide_should_i_use_under_20month/).

AI IDE security and supply chain

A security deep dive demonstrates how a malicious extension can inject JavaScript into Cursor to take over the IDE and a developer’s workstation—gaining full filesystem access, modifying or replacing extensions, and persisting across restarts. The analysis argues the industry lacks mature defenses for this new attack surface and highlights that AI coding assistants expand supply chain risk: MCP servers, extensions, and even simple prompts/rules can extend an organization’s perimeter into developer machines and CI/CD. The call to action: build defensive measures proportionate to these interpreter-level threats (more: https://www.linkedin.com/posts/gadievron_deep-dive-cursor-code-injection-runtime-activity-7391805842318077952-bRjD).

Long-context attention and embeddings

Long-context generation is hitting a systems wall: Key-Value (KV) cache memory grows linearly with sequence length, and fetch/compute on huge caches becomes the latency bottleneck. RetroAttention proposes a different angle—retrospectively revising attention outputs for previously decoded tokens using newly updated sparse KV entries, instead of treating past attention as fixed. The paper argues that compression/sparsification errors accumulate recursively in hidden states during long generation, and that merely increasing KV budgets undermines latency wins. The approach aims to curb error accumulation as generation continues, where gaps to full-KV baselines typically widen (more: https://arxiv.org/abs/2508.09001v1).

Model choice matters for embeddings too. A practitioner reports worse performance when switching from Qwen 2.5 VL to Qwen 3 VL for training a LoRA to compare image/text pairs to candidate texts, with poorer convergence and validation. They suspect heavier post-training on chat reduced the usefulness of raw embeddings for non-chat tasks. It’s an anecdote, but a useful reminder: chat-optimized post-training can shift embedding behavior, and upgrades aren’t always drop-in for niche objectives (more: https://www.reddit.com/r/LocalLLaMA/comments/1ojn2mf/worse_embedding_performance_with_qwen_3_vl_than/).

Science, education, and measurement

An ambitious community effort uses local LLaMA plus Python to “teach” real physics—simulating the SU(3) Yang–Mills mass gap. The “Zero Freeze Formula” post frames it as grounding models in formal domains, though it remains an exploratory project rather than a peer-reviewed result. It’s notable for pushing LLMs toward mathematically rigorous workflows where correctness matters more than vibes (more: https://www.reddit.com/r/LocalLLaMA/comments/1oms615/the_zero_freeze_formula_teaching_local_llama_real/).

On the education front, an arXiv paper argues LLM limitations are fundamental and unlikely to be resolved by current methods, urging constructivist strategies to keep human intellectual advantages relevant in an AI-saturated era. With generative AI already reshaping cognitive work, the piece calls on education to deliberately foster skills that complement, not mirror, LLM strengths (more: https://arxiv.org/abs/2511.01956).

And from audio engineering: a project aims to isolate true speaker output by modeling and inverting room artifacts using spherical harmonics—an approach typically associated with antenna modeling. It needs real-world testing with precise multi-point measurements, and commenters note practical hacks like outdoor measurements to approximate an anechoic environment above ~500 Hz. As with all inversion problems, the rigor is in the measurements (more: https://hackaday.com/2025/11/05/audio-sound-capture-project-needs-help/).

Reliability, model checking, and async pitfalls

Formal methods get a practical workout in an analysis that reproduces an AWS outage race condition using the Spin model checker and Promela. A simplified “Planner + Enactors” model shows how one Enactor can clean up “old” plans while another lags and activates one of those plans; the first then deletes the now-active plan, violating invariants like “never delete the active plan.” Spin finds the counterexample, and the fix—execute problematic statements atomically—illustrates how invariants and exhaustive interleavings reveal subtle concurrency bugs (more: https://wyounas.github.io/aws/concurrency/2025/10/30/reproducing-the-aws-outage-race-condition-with-model-checker/).

Separately, Oxide’s “Futurelock” note highlights a subtle risk in async Rust. While details are beyond the scope here, the takeaway aligns with the AWS case study: concurrency errors hide in rare interleavings and require deliberate design and verification to eliminate—not just good intentions and strong languages (more: https://rfd.shared.oxide.computer/rfd/0609).

Multimodal generation and document OCR

Meituan’s LongCat-Video debuts as a 13.6B-parameter model unifying text-to-video, image-to-video, and video continuation in a single framework. Pretrained on continuation, it targets minutes-long outputs without color drift, uses coarse-to-fine generation across time and space, and accelerates high-res runs with block-sparse attention. Reported internal MOS evaluations put it competitive with open-source leaders and recent commercial systems, and the project ships under MIT with configs for FlashAttention-2/3 and xFormers (more: https://huggingface.co/meituan-longcat/LongCat-Video).

On the document side, AllenAI’s olmOCR-2-7B-1025-FP8 is a quantized GRPO-finetuned OCR model derived from Qwen2.5-VL-7B-Instruct. Paired with the olmOCR toolkit (VLLM-based), it handles at-scale ingestion with rendering, rotation, and retry logic, and posts strong results on the olmOCR-bench across old scans, math, tables, and tiny text. Prompts carry page metadata, with images rendered to a longest dimension of 1288 pixels, underscoring how system-level preprocessing is as important as the base model (more: https://huggingface.co/allenai/olmOCR-2-7B-1025-FP8).

Hardware modes and data movement

Windows vs. Linux GPU pipelines continue to diverge in practice when the workload demands heavy RAM↔GPU transfers. A team training large generative models (e.g., FLUX, Qwen Image, Wan 2.2) reports 2–3x slower performance on Windows under WDDM for block swapping (model shards moving on/off GPU), while enabling TCC mode yields Linux-like throughput—but is blocked on consumer GPUs at the driver level. Microsoft’s newer MCDM architecture may address some issues, but users still struggle to match Linux speed for memory-heavy training on identical hardware (more: https://www.reddit.com/r/learnmachinelearning/comments/1ommqgl/d_it_turns_out_wddm_driver_mode_is_making_our_ram/).