https://cybersec.org.za/research.html #AI #cyber #security #dev #Africa #LLM #context #rot #cyber

https://cybersec.org.za/papers/dcr-bounded-attention.pdf

Dynamic Context Runtime: Bounded Attention over Unbounded History

Language models degrade as stale and superseded material piles up in their context. Making the window bigger does not fix it. This report describes a runtime that keeps history unbounded and attention bounded — storing everything as immutable spans and a typed provenance graph, then assembling a small working set each turn by solving a knapsack under an explicit token budget. It includes a zero-dependency Rust implementation and an ablation that names which mechanisms are actually carrying the result.

4.19M tokens of history

235 tokens per query

7 / 7 probes answered

17,835× less attention than the full transcript

48,651 state nodes held

28× history growth, flat working set

13,721 lines of Rust

0 external dependencies

152 tests

5 controls found that could not fail

0.4% of stored spans ever rendered to the model

47% cheaper with a mechanism the paper argues for switched off

Hey everyone,

I have been using OpenCode for a while now. It's pretty great, but there was this one thing that kept bugging me: I couldn't easily create custom reliable workflows and pipelines. For a while, Opencode was one prompt and one model at a time. So, I created OpenFlow, a very minimalist open-sourced project that allows you to orchestrate a pipeline of agents while still connecting your own API keys. I forked it from OpenCode, so you still have the harness, skills, and overall ability. I published it recently and am still often improving it. I would love to hear some feedback of what you guys thought of it and how I can improve it. Thanks!

Link: https://github.com/SeeRay11/OpenFlow

Hey everyone,

Over the past few months, I’ve been working on Ghostlink, an open-source distributed inference fabric designed to harness whatever mixed hardware you have lying around on your local network.

Instead of assuming a homogeneous fleet of high-end GPUs, Ghostlink pools together heterogeneous machines (like a gaming rig, an older laptop, an NPU-equipped ultrabook, or a Mac) into a single unified inference cluster—without requiring complex YAML setups or manual RPC flags.

It’s completely open source under the MIT license. Check out the Ghostlink GitHub repository to view the benchmarks, architecture breakdown, or run it locally.

I’d love to get feedback from the community on hardware setups or features you'd like to see next!

I've been building TokenMizer, a local proxy that sits between your editor/CLI and whatever model you're using (Claude, GPT, Ollama) and handles two things I kept re-solving by hand: session checkpoint/resume, and a graph-based memory instead of a flat transcript.

The problem: once a long agent session hits the context limit, the usual fix is summarization, and summaries lose the reasoning behind a decision, not just the decision itself. I'd see a summary saying "switched to Argon2" with no trace of why bcrypt was rejected, so the agent would re-litigate the same tradeoff two sessions later. Flat transcripts have the opposite problem: everything is kept, but nothing is prioritized, so retrieval is just recency-biased keyword luck.

What TokenMizer does differently: instead of one growing text blob, decisions, constraints, and open questions are stored as nodes with edges (this decision depends on that constraint, this question was resolved by that decision). Checkpointing snapshots that graph plus a resumable session state, so you can kill a session and pick it back up without replaying the whole history through the model again.

Where it's rough: there's no eval harness yet comparing retrieval quality against a naive flat-transcript baseline, so right now my evidence is anecdotal (my own sessions), not benchmarked. I also learned the hard way that benchmarking your own memory system by asking it questions only it can answer is circular, so I'm holding off on publishing numbers until I have an honest comparison.

Repo: github.com/Shweta-Mishra-ai/tokenmizer (I'm the author). It's a Python project, MIT licensed. If you've hit the same summarization-loses-reasoning problem, I'd be interested in how you're handling it, and PRs/issues on the eval-harness gap would genuinely help.

Hello Open Source community,

we’ve been building scibly an AI-native learning platform. The idea is pretty simple. You give it existing material and knowledge like your docs, blog posts, PDFs, ... and it creates an interactive course from it. You can edit everything afterwards and share the result anonymously or to your invited users.

Scibly is AGPLv3.

Video demo: https://www.youtube.com/watch?v=TcpLUNBRhQw

GitHub: https://github.com/scibly-dev/scibly

We appreciate all your feedback

I’ve been looking at a fairly basic question in weight-space learning that I don’t think gets separated cleanly enough:
Why does reading semantics directly from neural network weights work pretty well when the networks share an initialization, but collapse when the networks are fitted independently?
The usual explanation is parameter symmetry. Permute hidden units, flip equivalent signs, etc., and two parameter vectors can represent the same function while looking completely different to a downstream model.
But there are actually several different claims hiding in that explanation:
the parameterization has a symmetry group,
accounting for that symmetry improves weight-space prediction,
the symmetry is actually sufficient to explain the observed degradation between shared-init and independently fitted networks.
Those aren’t equivalent, so I tried to measure them separately.
The setting is SIREN-style implicit neural representations.
For a hidden sine neuron, the relevant function-preserving transformations generate the infinite dihedral group
D_inf = Z semidirect_product Z_2
and including neuron permutations gives the layer action
D_inf wr S_n.
For one hidden layer, I prove generic identifiability modulo this group using the distributional Fourier transform of the realized function.
Roughly, the Fourier transform becomes an atomic measure supported at the incoming frequencies +/- w_i, which lets you recover the parameters up to exactly the D_inf wr S_n action under explicit genericity conditions.
One consequence is that this isn’t just the usual permutation/sign story. Integer-pi phase transformations are affine rather than linear, so they aren’t captured by symmetry descriptions restricted to monomial matrix actions.
At depth two things get more annoying because a neuron’s outgoing weights are simultaneously acted on by the next layer. I ended up constructing exact cross-layer invariants by coupling the layers through the second-layer Gram matrix instead of treating neurons independently.
The empirical part then uses roughly 1.8 million fitted INRs across MNIST, FashionMNIST, and CIFAR-10, with controlled protocols separating shared initialization, optimization stochasticity, and independent initialization.
The result I found most interesting:
Randomizing only the exact symmetry group, while keeping each network’s represented function fixed, destroys 79.1 of the 80.4 accuracy points in the MNIST shared-init vs. random-init gap.
I want to be careful about the interpretation here.
This establishes sufficiency: symmetry scatter alone can reproduce almost the entire degradation.
It does not establish that 79.1 / 80.4 of the naturally occurring gap is causally mediated by symmetry. Those are different estimands.
Breaking the group apart, sign flips account for roughly 63 points of that induced loss, neuron relabeling about 15, and integer phase shifts about 1.
There was another result that changed my interpretation of the problem quite a bit.
A reader that directly quotients the D_inf wr S_n structure on the raw parameters reaches 0.917, compared with:
0.628 for the best orbit-valued reframing,
0.526 for the same reader family over a fixed invariant encoding,
0.265 for a permutation-equivariant baseline.
But when I FLOPs-match weight-space inference against simply querying the INR as a function, the function-space route is still much better:
95.3% at 1.6 MFLOP using 64 learned query coordinates
versus
64.4% at 5.5 MFLOP for the best weight-space rung on that frontier.
That leads to what I think is the more interesting conceptual question:
If a complete invariant is informationally equivalent to access to the realized function, then the strongest justification for operating directly in weight space may ultimately have to be computational rather than informational.
Everything is public here:
https://github.com/ITheClixs/project-siren-gap
The repo includes the paper, implementation, tests, pre-registrations, lab notebook, prediction ledger, claims ledger, and experimental results.
I’d particularly appreciate criticism on three things:
whether the sufficiency/mediation distinction is being drawn correctly,
whether anyone sees a counterexample or missing assumption in the one-hidden-layer maximality argument,
whether there is related work on affine symmetry groups of periodic-activation networks that I’m missing.
Also very interested in attempts to break the invariants or reproduce the group-randomization result.
If something here is wrong, I’d rather find out from someone trying to kill it.

I’m building MARGINAL, an open-source runtime governor for AI coding agents.

The problem I’m targeting is simple: agents can keep spending tokens, calling tools, re-checking things, or choosing expensive actions without enough evidence that the extra work is actually useful.

MARGINAL sits in the loop and asks a different question:

Is this next action worth spending compute on?

I just rebuilt the demo so it’s no longer a marketing page. It’s an actual interactive browser simulation.

You press RUN THE SAME TASK and two agents start from the exact same broken Python workspace at the same time:

WITHOUT MARGINAL
Executes every candidate action.

WITH MARGINAL
Scores the same candidates before execution and either:

FUND + EXECUTE

or

REJECT BEFORE SPEND

You can pause it, advance step-by-step, reset it, or run it at different speeds while watching tokens, calls, estimated cost, declared latency, workspace state, and MARGINAL’s decision reasoning update live.

Both sides must reach the same verifier PASS.

The included deterministic fixture currently ends at:

72,800 → 4,300 declared tokens
9 → 3 actions
PASS → PASS

Those are declared deterministic demo costs, not provider telemetry or a claim of 94% savings in real workloads. The point is to make the governance mechanism inspectable instead of hiding it behind a benchmark number.

MARGINAL is open source, local-first, provider-neutral, and starts from the principle:

Observe first. Prove waste. Earn enforcement.

Repo:
https://github.com/SignalLayerLabs/Marginal

Interactive demo:
https://signallayerlabs.github.io/Marginal/demo/

I’m especially interested in people trying to break the decision logic, finding cases where an action MARGINAL rejects was actually valuable, or contributing adapters for other coding agents.

New Row-Bot demo: turning your inbox into an action plan.

​

Row-Bot checks important emails, finds action items, drafts replies, creates calendar events, and schedules reminders, with approvals for sensitive actions.

​

Not just chat. Real workflow automation.

​

[https://github.com/siddsachar/row-bot\](https://github.com/siddsachar/row-bot)

I made another whitepaper on AI infrastructure, it's a bit more nische than the last one, but i hope someone might appreciate it.
Github - https://github.com/gurrakeller/Europes-Sovereign-AI-Migration
My DM's are always open if you have feedback or simply wish to discuss a topic in the paper!

I think we all went through this. Claude finished a task, told me it was done, and left me with 14 changed files and no idea which one mattered. The diff was accurate and useless. So I built the thing I wanted to be looking at instead.

Flare is a desktop IDE (Electron) where the main surface is a live graph of your codebase, every file a node, every import an edge, with a terminal underneath where you run claude, codex, or opencode. As the agent edits, the graph updates in real time.

The parts that are actually different from "another editor":

Activity, as it happens. Nodes light up the moment the agent writes to them and decay as they cool, so you're watching the shape of the work instead of a scrolling transcript. You can see it circling the same three files for the fifth time, or wandering into auth when you asked about the CSV parser. Changes are attributed per agent: the process tree of every terminal is watched, so if you have two running, you know which one did what. Files that changed and no human has opened since stay marked until someone actually reads them.

Blast radius before you touch anything. Hover a file and its dependents light up. shared/types.ts with 63 files downstream looks different from a leaf file, without you having to know that in advance.

A review tab that answers "did anything check this?" Flare sees both the file writes and the commands run in its own terminals, so it can say the tests ran, then two more files were edited and nothing re-ran, quoting the output line the verdict came from.

Agent smells. Rules for shortcuts agents take and humans usually don't: a test edited in the same burst as the code it covers, assertions deleted, .skip added, type suppressions introduced, coverage thresholds lowered.

Risky changes come to you. If the agent rewrites something load-bearing while you're looking elsewhere, it queues an alert in the corner. Reviewing it opens the actual red/green diff.

Undo that isn't git. Every change burst is snapshotted into a hidden shadow repo (separate GIT_DIR, your worktree). Revert one file, revert the burst, or jump back to the last state whose checks passed. Your real repo is never touched.

A task board the agent works from. Kanban lanes, but the cards are written to be handed off. "Copy for agent" emits the brief plus the files it names plus what the graph knows about them (29 files downstream, 0% covered, in an import cycle), so the agent starts from the map instead of rediscovering it. File a card straight from a graph selection with right-click → New task with these files. This directly tells Claude to not wander around out-of-scope files

MCP server, ~16 tools. The same lanes are queryable, so an agent can run its own loop: tasks_list to pick up work, task_get for the exact brief, task_update to log progress and move the card to review, task_create to file follow-ups it finds but shouldn't do now. Cards move on the board live while you watch. Plus impact_of (what breaks, and which tests to run), dependents, find_path, verification_status, and record_intent, which lets the agent state the goal before editing so whoever reviews the diff isn't reconstructing why it exists.

Runs in a browser too. Same bundle, same backend, over a websocket, because the backend has to run on the machine the agent runs on and that's often not your laptop. One port, token auth, works behind Codespaces / Gitpod / a JupyterHub proxy.

Completely open source with MIT license, Node 20+. Built with agentic coding, which is exactly how I ended up needing it. Test it out and leave a star if you find it helpful, I will package it very soon to make it easier to install!

https://github.com/AlgoNoRhythm/Flare

I was tired of overly priced Pomodoro apps that lock basic features behind subscription paywalls and end up distracting you more than helping you focus. So, I built FocusSpace—an open-source, AI-native focus app designed from the ground up to protect your deep work.

FocusSpace is built with a singular philosophy: Everything you need to get into flow, and absolutely nothing you don't. It pairs a distraction-free, beautifully minimalist interface with state-of-the-art AI orchestration—giving you complete ownership over your attention and your data.

comes with following features

• Agentic Task Control: Instead of manual tracking, you can let a Hermes agent—or your own custom local model—manage your boards, break down complex goals into subtasks, and hold you accountable.
• Built-in AI Co-Pilot: Just describe what you want to achieve in plain English, and the onboard AI automatically structures your sessions and schedules your workflow. (use your own llm api key)
• Distraction-Free Environment: Features living, code-generated wallpapers (like rain or starfields) alongside a single-tap Focus Mode and mini-player designed to stay out of your way.
• Integrated Audio Controls: Built-in Spotify integration lets you search playlists and control focus audio directly inside the app so you never break your flow.
• Deep Work Analytics: Keep track of your real focus time over days and months with clean heatmaps, session timelines, and streak counters.
• Open Source and Self-Hostable: You have total control over your data. Host it yourself locally, deploy your own instance, or use the free hosted web version.

try it out

• 💻 Live Web Version:focusspace.live
• 📦 GitHub Repository:github.com/EntangledQuantum/focusspace