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31 cards in the AI & Agents domain — same language, same primitives, ready to compose.
The queue an autonomous run stops at when it needs a human. A plan arrives with a countdown that will approve it on silence — hover the ring to revoke that, expand the fold to read all six steps — and behind it a three-question wizard whose last option is always a text field for the answer nobody offered. Skipping hands the agent its defaults; either way the queue ends in the decisions you actually made.
Replace the password form with e-mail links and device passkeys.Ships behind a flag with a one-command rollback.
An AI agent status card: a pixel mascot, live task progress on the shared tick meter with accruing tokens, cycling tasks, pause/resume, and an activity log.
Juno
EXECUTIVE ASSISTANT
CURRENT TASK
A multi-agent orchestration panel: six agents leave the queue on staggered delays, phase labels crossfade as bars advance, with pause/resume and a reset rewind.
Brief writer
Drafting the Q3 launch brief
Code review
Reviewing PR #217
Research agent
Competitor pricing sweep
Email drafter
Reply to the vendor thread
Data analyst
Q3 retention cohort
Web browsing
Collecting founder bios
0% avg progress
A supervisor holding three long-running agents, each lane an ECG that scrolls at its own resting rate. Tap one to kill it: the waveform collapses onto the baseline, a grace ring drains for three seconds while the watchdog waits for a heartbeat, then SIGKILL — and a new process answers on a new pid with the restart tally one higher. Every reading, down to the footer line, is a function of one clock.
A release poster for an open-weight model, built around the one thing it claims to be good at: a question in plain words, then the tool call typing itself out, the ✓ landing a beat later and `resolved` a beat after that. The deck rotates through three tools — a model that always reaches for the same one is not demonstrating tool choice — and the reference's brand green becomes `--primary`, so the poster reskins with the theme.
An AI run visualized: one stage color themes the whole card — logo, status, progress, step checks, workspace scenes — as it reads, searches, thinks and writes.
Ship the theme switcher 0 / 5
tokens.css
0 tokens$0.0000:00
A reference poster of the six agent-orchestration patterns — chaining, routing, parallelization, orchestrator, evaluator loop and autonomous — each a little node-and-connector diagram with an accent flow dot, an edge-case note and a one-line usage.
a failed gate stops the chain
fixed steps, each checks the last
classify first, then specialise
run at once, merge or vote
subtasks decided at runtime
rejected, back with the notes
write, grade, repeat until it passes
no fixed path, it decides when done
Four ways to deploy an agent — batch, stream, request, edge — read against the one thing that actually varies: the window between the work arriving and the answer being due. Each lane is the same left-to-right pipeline running its flow dot at its own speed, and each ends on the failure that comes attached to its shape.
the same agent, four answer windows — schematic, not measured
Nothing here is a preference. The window between the work arriving and the answer being due is set by the product, and it decides the rest: whether a queue can absorb a spike, whether replicas need warm models, whether there is a network at all. Pick the shape that fits the window, then live with the failure that comes attached to it.
One agent run as a five-node graph — Prompt, three Tools, Response — that a single clock walks through three stages: receiving the prompt, running the tools, composing the response. Nodes glow, connectors flow, the activity panel swaps, and the meter accrues tokens / cost / time.
W
The second layer of the agent lineage as a versus: one question, one repo, one history, and two ways to assemble them into the same 200K window. A twelve-turn clock walks both — the dumped side hits the ceiling at turn three and starts evicting material it will have to read again, while the curated side compacts and keeps its headroom. The counters are the argument, not the bars.
they part company here — what gets assembled
Paste it all
a bigger window will fix it
6 evictions in: the run is now paying to read files it already read.
Edit it
the window is the budget
2 compactions: transcript dropped, conclusions kept, nothing re-read.
Both designs fill up; only one can afford to choose what it loses. A window that starts at 88% has no compaction strategy available to it, only eviction — and eviction is how a run ends up paying twice for the same file.
Every panel ends with the codex line the failure produces, which is why this is not a list of warnings but an account of where a codex comes from. The entry requirement is that a failure survives careful reading — anything you catch by looking harder belongs in review, not here — so every catch names a mechanism rather than an attitude.
every one of these survives careful reading — that is the entry requirement
None of these is caught by being careful, which is why “review it properly” is not a plan. Each is caught by a mechanism, and each mechanism is one line long — so a codex is not a document somebody sat down to write. It is the transcript of the times you were fooled.
Six decision points rather than six tips: each panel shows the screen exactly as it appears, marks the ONE span to read, and reveals what a wrong yes costs. Five are the agent asking you; the sixth is you about to paste something, which is the only risk no permission prompt will ever stop.
five of them are it asking you — the sixth is you about to hand it something
A first day spent learning features produces someone who can drive. A first day spent learning where the wheel gets handed over produces someone who can be trusted with it — which is the only one of those two people a company can actually deploy.
The first layer of the agent lineage as a field guide: role, examples, output contract, reasoning-first, quoting the data, temperature. Every panel draws the same 15-column strip, so six techniques read as six reshapings of one thing — the room the prompt leaves the model — and one clock engages them in turn. Tap any panel to work it by hand.
None of these makes the model smarter. Each one deletes continuations you did not want — which is the only thing a prompt can do to a machine whose job is to continue text.
A reference poster of the eight ways an agent loop should stop — goal met, turn cap, budget cap, wall clock, no progress, human interrupt, error threshold, external event. One clock replays every panel on a shared run timeline: the playhead hits its threshold marker, a halted pill pops with a ripple, and the cycle resets.
the one exit teams design for
catches the task that would never finish
the exit that cancels the 3am invoice
for runs that outgrow their deploy window
busy every turn, changing nothing
a kill switch within actual reach
stop retrying into the same wall
the work got done somewhere else
A reference poster of the ten evals an AI engineer reaches for — golden set, LLM-as-judge, rubric scoring, trajectory eval, tool unit tests, regression suite, A/B in prod, human review, shadow run, red team. One clock replays every panel: a playhead marks pass/fail, a draft travels to a judge that counts up, a trajectory flags the wrong tool call, traffic splits A/B, and a red-team attack slips past the guardrail.
A reference poster of everything an agent does before it answers — renders the prompt, picks the context, trims the middle, loads tool schemas, chooses one tool, guesses its arguments, waits on the call, truncates the result, retries in silence, grades itself, picks a branch, compacts the history, writes to disk, spawns a subagent, and shows you one line. Elevated over the reference: the fifteen panels are staged on one clock so the poster reads as a single turn walking its pipeline, and a turn rail tracks the live stage.
template plus variables, not what you typed
which files and history get in
what did not fit is simply gone
every tool you defined costs tokens
one call, out of everything available
the path it guessed, not the one you meant
the latency nobody attributes correctly
42k of output, 900 tokens kept
the failure you never saw happen
only if you wrote a rubric
continue, retry, or stop
your turn 3 becomes one sentence
the only part that survives a crash
burns 40k, hands back 1k
the one line you actually read
One context window read as an attention U-curve: the system prompt and the question you just asked sit at the strong edges, the file you pasted sits in the trough, and a token strip underneath dims with the same curve. Elevated over the reference: the amber rule is a probe you can scrub anywhere in the window — reliability, colour, and the verdict at the foot of the poster all re-read as it moves, and "Strongest slot" snaps it to the position that survives.
One instruction, four fixed slots, and one rule you can move.
how reliably a position gets used
The system prompt
re-read on every single turn
strong
The file you pasted
six thousand lines of it
weakest
Tool output from earlier
climbing back, not all the way
fading
The question you just asked
the last thing it reads
strong
The system prompt
re-read on every single turn
The file you pasted
six thousand lines of it
Tool output from earlier
climbing back, not all the way
The question you just asked
the last thing it reads
Your rule is parked in the blind spot
you did not write a weak rule. you put a good rule in a weak slot.
Two verification chains from the same task, drawn side by side: identical for three steps, then one loops the output back into the agent that wrote it while the other hands it to a test that never reads it. Elevated over the reference: a scenario switch decides whether the output is actually correct — flip it to wrong and the external chain turns rose and blocks, while the self-review chain is unchanged and still accepts.
Who is allowed to say the work is done.
Self-review
the agent checks its own work
the task
what was asked
the agent
one context window
the output
a diff, an answer, a plan
the same agent reads it
same weights, same window
it looks correct
generated, not measured
accepted
on the strength of a sentence
a second opinion from the same source
drawn from the same context that produced the output
External check
something outside decides
the task
what was asked
the agent
one context window
the output
a diff, an answer, a plan
a test runs
it never reads the output
pass or fail
the suite came back green
accepted
on the strength of a signal
evidence the agent cannot write
it ran, and it came back green
The method behind a 535,496-line port done in eleven days with 64 agents, as a working graph: a rulebook every worker starts from, one compile hoisted out of the loop, four isolated worktrees, two blind reviewers, and the edge that matters — a rejection routes back into the rules, not the file. The graph is coloured by which principle governs each part, so selecting a rule lights exactly its region; rule 2 gets a live proof — kill the run and it resumes where it died.
Don't drive the agents from a chat. Draw them a graph.
A precision picker: the 8×8 weight grid re-quantizes to coarser shades as bit depth drops, metric bars re-scale, and the fit verdict flips against the GPU budget.
WEIGHTS
4-bit / weight
4 shade levels — fewer bits store coarser values.
Fits comfortably on 16 GB
6.0 GB needed · 10.0 GB free
Q4_K_M — best size / quality balance
A model-training monitor: train/val loss curves grow point by point past epoch checkpoints while metric tiles re-compute live, completing into a deploy state.
LOSS
2.432
VAL ACC
31.0%
LR
3.00e-4
GRAD NORM
1.38
A five-stage pipeline color system binds the neuron diagram, the numbered steps and the formula tokens — click a step to trace it; four levels of original copy.
Passing little notes down the line
Imagine a room full of tiny workers holding strings. When a cat picture comes in, the first workers shout what they spot — pointy ears! whiskers! — and pass notes to the next row. The last row reads every note and yells the answer: cat! Each time they get it wrong, they swap a few strings around so the notes come out better next time.
It's a team game that gets sharper with every round.
ŷ = σ(w₁x₁ + w₂x₂ + w₃x₃ + b)
Perceptron
1958
~1K PARAMS
CNN
1989
~1M PARAMS
LSTM
1997
~10M PARAMS
Transformer
2017
10⁸–10¹² PARAMS
PARAMETER BARS ARE LOG-SCALE — EACH STEP IS ROUGHLY 1,000×
An agent run replayed on a Gantt timeline: playback reveals each step by its real start/duration, model steps count tokens up, Show tool I/O expands every call, and Raw trace crossfades to an event log.
research-agent-v2 · 9 steps · 9.71s
The strategy the planner weighed: a branch diagram tracks the selected candidate, clicking any of the four inspects its reasoning and utility score, and the branch the agent actually took keeps its PICKED chip.
The agent considered 4 strategies and picked the highest-utility branch.
SELECTED REASONING
The three sources are independent and rate-limit safe, so fetching them at once collapses wall-clock from ~6.4s to ~2.1s at negligible extra cost — the best latency-for-cost trade available here.
A task relayed through four agents, replayed from one clock: the agent holding the baton wears a breathing halo, finished ones keep a check in their own colour, and the rail under them is a relay — each segment wears the colour of the agent it leaves, so the finished path reads back as the hands the task passed through.
Two retrieval architectures side by side as living circuit diagrams: blue is the RAG machinery, green the cache-augmented variant, amber the one query/answer path threading both. Every edge carries a pulse, and all pulses leave in phase — one synchronized tick of data through both pipelines, looping forever with zero JavaScript (SMIL riding the edge paths).
Retrieval Augmented Generation
Retrieval Augmented Generation + Cache Augmented Generation
The picker itself, hosted in a mock composer: a chip opens a popover whose track has two forms — a plain rail with stop dots for the normal levels, and the reveal at the top stop, where the whole rail turns into a shimmering dot matrix and the name rolls to accent.
Type / for commands…
A live agent-run feed: a new row streams in every ~1.6s with a motion enter, the list caps and drops the oldest, Pause freezes the stream, and an Errors filter isolates retries and tool errors.
run_7c1e · 14:32:18
1.4s
12.4k tok
run_2a90 · 14:32:14
340ms
2.1k tok
run_9f03 · 14:32:09
4.8s
38.9k tok
run_11b7 · 14:32:03
2.1s
18.7k tok
run_4d6c · 14:31:57
780ms
5.4k tok
run_08a2 · 14:31:50
1.6s
9.8k tok
run_63f1 · 14:31:44
3.0s
24.5k tok
run_5e7d · 14:31:39
210ms
1.3k tok
Token usage across models: one stacked share bar over a sorted legend, hovering either cross-highlights the matching segment and dims the rest, and the six shares always sum to 100%.
A monitoring rule set: each row pairs a condition with a severity and a live toggle; the segmented filter narrows by severity with a layout animation, firing rules pulse, and disabling one drops it from the footer's live count.
8 of 10 enabled
Success rate dropped
success_rate < 95% over 5m
2m ago
last fired
Latency p95 elevated
p95_latency > 5s over 10m
14m ago
last fired
Token spend spike
spend_rate > $200/hr
58m ago
last fired
Tool error rate
tool_errors / runs > 2%
4h ago
last fired
Timeout cluster
timeouts > 10 in 5m
3h ago
last fired
Eval regression
eval_score < baseline − 0.05
5h ago
last fired
Loop detection
same_tool_calls > 8 in run
18h ago
last fired
Budget at 90%
monthly_spend / budget > 0.9
—
never
Cold start spike
p50_cold_start > 3s
3w ago
last fired
Schema drift
tool_schema_hash changed
—
never
A live incident feed: the featured incident plots a metric breaching its threshold with a dashed reference line and a now-dot; clicking a row promotes it with a crossfade + redraw, Acknowledge resolves it in place, and the filter isolates the open ones.
6 in 24h · 4 open
on research-agent-v2
Success rate fell to 87.4% over the last 5 minutes.