This is part sixteen in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. The 0.8.0 release notes cover the storage and pipeline changes that shipped alongside this rewrite; Part thirteen covers how the new profiles.improve config drives the improve pipeline. Config files are where projects go to accumulate technical debt quietly. Each new feature gets a new key. Each new key gets a new parser. Each parser has slightly different error handling, slightly different defaults, and slightly different ideas about what "invalid" means. Nobody notices until a user files an issue that says "I had a typo in my config and akm just silently used defaults for three weeks." That was the state of akm's config layer going into 0.8.0. What the Old Shape Looked Like The v1 config had three top-level blocks that grew independently over two years: llm.* for LLM connection settings, agent.* for agent process settings, and llm.features.* boolean flags gating per-feature LLM calls. The features block was nested under llm for historical reasons even though many features used the agent, not the LLM. The agent's per-process map lived under agent.processes , while LLM-gated features used llm.features.index.metadata_enhance style dotted paths. Each block had its own parser function. parseLlmConfig , parseEmbeddingConfig , parseIndexConfig , and a dozen more. The comment at the top of the new config-schema.ts is blunt about it: the Zod schema "replaces the ~1.4k LOC of legacy per-shape parsers." The problems that accumulated in that ~1.4k LOC: Unknown keys were silently accepted. If you wrote llm.temperaure (typo), the parser ignored it and fell back to the default temperature. No warning. You tuned a key that did nothing. Bad JSON was masked. The config loader caught JSON parse errors and fell back to DEFAULT_CONFIG — the compiled-in defaults. Your entire config file could be corrupt and akm would start without complaint, using defaults a

This is part fifteen in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. Part ten introduced the improve pipeline and how it generates proposals. Part twelve covered belief-aware memory, which feeds directly into the confidence scores covered here. The fundamental problem with agent-generated stash updates is trust. You want to capture what the agent learned — the debugging insight from last Tuesday's session, the architectural pattern it derived from reviewing twenty PRs — without blindly writing unreviewed content into the knowledge base your other agents depend on. One bad promotion and you've contaminated search results with a hallucinated fact that will keep showing up until someone notices. akm's proposal queue is the answer to that problem. Introduced in 0.7.0 and extended in 0.8.0, it separates generation from promotion. Every agent-driven change writes to a durable queue first. Nothing reaches your live stash until you explicitly accept it. The queue is the safety net. How the Queue Works When akm improve or akm propose runs, the output goes to the proposal queue — not to your stash. Proposals live outside the asset tree. They never appear in akm search results and never get indexed alongside your real assets. The quality: "proposed" marker ensures this at the database level: proposed assets are excluded from default search and only surface through the akm proposal * commands or an explicit --include-proposed flag. This means an agent can generate dozens of proposals in a single akm improve run and none of them affect your live stash until you decide they should. Multiple proposals for the same ref coexist without filesystem collisions. You can review them at your own pace, reject the bad ones, and accept the rest in whatever order makes sense. The complete review workflow: akm proposal list # see what's pending akm proposal show < id > # render the full proposal content akm proposal diff < id > # dif

Introduction One of the common tasks performed by DevOps Engineers is upgrade of their organization's Kubernetes Cluster at least once every 3 months as Kubernetes release newer version while maintaining on the last 3 released versions. For instance, if the newest version is v1.34, the supported versions would be v1.34, v1.33 & v1.32. Hence, the need to understand how this upgrade process can be achieved with zero downtime. Prerequisites: Cordon your Nodes: This simply means making your nodes unschedulable. No new deployments would be scheduled on the node. Review and understand the change logs in the release notes - Ensure that the change logs or updated components won't affect your production environment. Kubernetes upgrade are irreversible - You can't downgrade your cluster after an upgrade. A fresh installation would be required in the event of an issue with the upgraded version. Hence Lower Level Environment Test (Unit, Staging or Pre-Production) - Given that Kubernetes upgrades are irreversible, always test the newer version and allow monitoring for about 2-weeks before production cluster upgrade. Control Plane & Nodes should be on the same versions. Cluster Auto-Scaler: If you are using this feature within your Kubernetes environment, ensure that it is on the same or compatible version with your control plane to avoid issues during the cluster upgrade. IP Addresses: Make available at least 5 IP addresses within the cluster subnet. Kubelet: This component should also match the version of your control plane before the upgrade. What are the actual upgrade processes Control Plane Upgrade: If using the Managed Kubernetes Cluster (EKS, AKS, GKS), the Cloud Company will take care of managing the control plane. However, upgrade of the cluster doesn't happen automatically. Hence, you will be required to action this via the CLI, UI or EKSCLI etc. Node Group or Data Plane Upgrade: Managed Node Groups - This is easier because you can use the rollout deployment approach,

This post is part of the akm-knowledge series. Part ten introduced the improve pipeline — what each phase does and how to schedule it. This post goes deeper on what continuous operation looks like in practice: the hardware numbers, the reliability bugs we hit at 48 runs per day, and the observability layer we built to keep watch. Most people think of AI agent memory as something that happens during a session. You talk to your agent, it learns things, maybe you save a few notes, the session ends. The next session starts cold. akm improve is built around a different model: a continuous background process that runs on your own hardware, against local models, and quietly curates your agent's knowledge base while you work on other things. No cloud API required. No per-token billing for the maintenance pass. A GPU you already own, a model you already have downloaded, running on a schedule. This post covers what 24 hours of autonomous operation actually looks like, how consumer-grade GPUs handle the load, the reliability work that makes continuous operation viable, and the observability layer that lets you know it's working without watching logs. What akm improve Does in 24 Hours akm improve is a multi-phase pipeline. The core pass — consolidation — loads your memory pool, groups related memories into chunks, sends each chunk to a local LLM for a consolidation plan (merge similar memories, promote high-signal ones to your stash, delete redundant ones, surface contradictions), and then executes those plans. After consolidation, memory inference runs a lightweight factual extraction pass, and graph extraction updates the entity-relation index. The pipeline is scheduled to run automatically. Here is what one 24-hour window produced: Metric Value Runs completed 48 / 48 — zero failures Memories processed 14,189 Promoted to stash 1,361 Merged (deduplication) 49 (64 secondaries absorbed) Contradictions surfaced 211 Deleted (redundant) 31 Memory inference yield 69.3% — 115 new ato

This is part thirteen in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. Part ten covered the full improve pipeline — all five phases and how they connect. Part fourteen covers what 48 runs per day looks like in practice, including hardware benchmarks and the reliability bugs that surface at that frequency. The reflect pass inside akm improve has three execution modes. Most installs are still running the slowest one. Agent mode — the original — spawns an opencode or claude subprocess for each reflect call. The subprocess starts cold, acquires a session, assembles context, makes its LLM call, and exits. That cold-start overhead is real: each call takes approximately 30 seconds on a quiet machine. Run akm improve against a 69-ref stash and the reflect phase alone costs about 35 minutes. SDK mode eliminated the subprocess. The reflect call runs in-process, cutting per-call latency to 10–15 seconds. A 69-ref run drops to 12–17 minutes — better, but still bounded by round-trip overhead that the reflect task does not actually need. LLM mode removes the round trip entirely. The context for reflect is statically pre-assembled — no live tool calls, no file reads, no external context needed. A direct HTTP call to the LLM endpoint is sufficient, and it costs 6–10 seconds per call. A 69-ref run completes in 8–10 minutes. Mode Per-call latency 69-ref run agent (CLI subprocess) ~30s ~35 min sdk (in-process) ~10–15s ~12–17 min llm (direct HTTP) ~6–10s ~8–10 min The 3–4× end-to-end improvement is from eliminating overhead that was never necessary for what reflect does. Why Reflect Does Not Need an Agent The reflect pass takes a stash asset, examines its current content, and proposes a refined version. The inputs are fixed before the pass starts: the asset text, its metadata, and the improvement prompt. Nothing changes mid-call. No files need to be opened. No search queries need to fire. No external context needs to be pulled

A self-improving memory loop sounds like a clear win until you watch it rewrite something correct with something outdated. The agent remembered a fact. You verified it. A later consolidation pass ran against a stale context window, decided the memory was imprecise, and replaced it with a weaker version. The original was better. You lost ground. This is the failure mode that belief-aware memory was built to prevent. Not "agents write wrong things" — that's a model quality problem. The specific failure is: the improve loop, running unsupervised, overwrites correct content it should have left alone. A loop that can degrade its own best work is worse than no loop at all. akm 0.8.0 ships captureMode and beliefState as first-class frontmatter fields on memory assets. Together they tell the consolidation pass what each memory is, what the agent believes about it, and whether it is eligible to be rewritten. The Two Capture Modes Every memory asset now carries a captureMode field. It has two values. hot means the memory was written or explicitly confirmed by a human. The improve loop treats hot memories as read-only. No consolidation plan, no merge proposal, no rewrite. If every memory in a chunk is captureMode: hot , the consolidation pass skips the LLM call for that chunk entirely — the chunk is counted as judgedNoAction before a single token is spent. This is the all-hot chunk early-exit. background means the memory was generated by an agent — promoted during a prior consolidation run, written by an inference pass, produced by akm remember without explicit human review. Background memories are eligible for improvement. The consolidation pass can propose merges, rewrites, deletions, or upgrades. When no captureMode is set, the memory is treated as eligible for consolidation. Memories that existed before 0.8.0 are treated this way on first encounter. --- captureMode : hot beliefState : asserted description : Primary LM Studio endpoint moved to Shredder (192.168.0.99:1234) -

This is part eleven in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. Part nine covered workflow assets and resumable procedures. Part ten introduced the improve pipeline that continuously curates your stash. Earlier parts addressed teams, distributed stashes, and community knowledge. Most automation with AI agents is reactive. You open a session, give the agent a task, wait for the result, close the session. The agent's clock runs when you run it. Task assets flip that model. A task is a YAML file in your stash that defines a workflow — what to run, when to run it, what environment it needs, and how long it's allowed to take. Once registered, the task runs on schedule without your involvement. The OS scheduler calls akm tasks run <id> , which executes the task and writes the result to state.db . You find out what happened when you check akm health or look at the log. This is the piece of akm 0.8.0 that makes continuous operation possible. The improve loop runs twice an hour because a task asset says it does. The hourly Discord health report fires because a task asset says it does. Neither requires an open terminal. The Task Asset Format Task assets live at <stash>/tasks/<id>.yml . The filename is the task ID. A minimal task looks like this: schedule : 0 * * * * command : akm improve --auto-accept 90 enabled : true That's enough to install a cron entry and run akm improve at the top of every hour. The full schema adds metadata and per-task timeout control: schedule : " 7,37 * * * *" command : akm improve --auto-accept 90 --timeout-ms 1620000 enabled : true timeoutMs : 1800000 name : akm-improve description : Run the improve pass at :07 and :37 — reflect, distill, consolidate, lint, and eval. when_to_use : Twice per hour; leaves ~23 minutes of idle headroom between completions. tags : - improve - maintenance The fields that matter most: Field Required Purpose schedule yes Standard cron expression. Maps to cro

This is part ten in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. Part nine covered workflow assets, vault assets, and the writable git stash. Part eight tackled multi-wiki support for structured research. Earlier parts addressed teams, distributed stashes, feedback scoring, and community knowledge. This one is about entropy. You ship a feature. Your agent writes several memories during the session — partial findings, a workaround, a note about the build step that kept failing. Those memories are accurate when written. Three sprints later, the workaround is no longer needed, two of the memories say slightly different things about the same subsystem, and the note about the build step refers to a CI config that was replaced. None of this is catastrophic. But it accumulates. After six months, a significant fraction of your stash is stale, redundant, or quietly wrong. You could audit it manually. In practice, you won't — the stash is too large, the relevance of any given memory is hard to assess without the context where it was created, and the judgment calls (merge these two? promote this? delete that?) are exactly the kind of work that's tedious for a human and tractable for an LLM. akm improve is the answer to that problem. It is a multi-phase pipeline that reads your stash, evaluates asset quality, consolidates scattered memories, extracts structured facts, and maps entity relationships — on a schedule, without manual intervention, producing proposals you can review before anything changes. The Five Phases akm improve is not a single LLM call. It is a sequenced pipeline where each phase produces inputs for the next. Reflect evaluates asset quality. For each asset in scope, the reflect pass reviews the content against usage signals — search hits, retrieval counts, feedback — and produces a quality assessment. Low-quality assets are flagged as candidates for improvement. Since 0.8.0, reflect can run as a dire

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Nintendo is planning to launch versions of Switch 2 hardware in the EU that will let users easily replace the battery. To meet its obligations from a new EU regulation that's set to go into effect on February 18th, 2027, Nintendo says on its website that it is "implementing measures to comply with these requirements […]

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