We track CPU load more precisely than our own day. After eight hours at the computer it's hard to answer a simple question: what did I actually do?
Not hours,
work state.
See how the day went —
not how many hours.
LogAgent captures day signals locally — focus, fatigue, context switches, breaks, posture, AI sessions — and explains why the day looked the way it did. Offline. No cloud.
How it worksTools know "how long".
Not "in what state".
An hour can be flow, procrastination disguised as research, a stall, or a call. App time alone explains nothing.
Read in the manifestoKnow: hours per app.
Miss: whether it was real work.
Know: what was planned.
Miss: what actually happened.
Know: steps and sleep.
Miss: five hours in the IDE.
Knows: meetings.
Misses: focus between them.
The problem lives between tools.
AI tools made the day fragmented
Cursor, Claude Code, Codex and console agents changed how we work. Days got more fragmented and cognitive load went up.
Read in the manifestoConstant switching
IDE, browser, terminal, the agent dialog — focus fractures dozens of times an hour.
High cognitive load
Prompt, review the result, fix, prompt again. From the outside it just looks like "browser and VS Code".
Invisible fatigue
A fragmented day builds burnout faster, and you only notice it after the fact.
Work state instead of hours
Not "how long in an app", but what state the day was in — and what follows from that.
Read in the manifesto"5 h at the computer, 61% productivity, 58% fatigue. Draw your own conclusions."
- →Numbers without context are not an explanation.
- →Unclear what to do with them.
- →One score hides the shape of the day.
"3.5 effective hours. Drop after 4 PM. Two 25-min focus sessions. Break skipped three times. Posture slipped after 40 min."
- ✓Every conclusion rests on clear signals.
- ✓Segments have confidence; disputed ones go to review.
- ✓You see not only how long, but why.
By period end, productive share grew 53%. Schedule important work for this phase.
Frequent switches — 64.3/h. Batch small tasks into one block.
Current fatigue 39%. Take a 20-20-20 break within 15 minutes.
An agent is about proactivity,
not passive tracking
All analytics run on rules and heuristics — local and explainable. No black-box single numbers.
Collects signals itself
Windows, domains, git, IDE, AFK, focus, breaks, camera — without your input.
Classifies with rules
Transparent heuristics instead of a magic score. Every segment can be explained.
Intervenes when it matters
Spots anomalies and nudges only when signals say it's needed.
One signal lies.
A series gives an honest picture.
The decision is not one sensor but their agreement. Disputed segments go to review automatically.
Ten sources — one coherent state assessment.
A camera that remembers nothing
Privacy-first is an engineering choice, not just ethics. Capture and analytics stay offline.
Not pomodoro on a timer
A nudge comes when signals say it's needed — not on a blind countdown.
- 20-20-20 for eyesContextual, by load and fatigue — not every N minutes blindly.
- Posture and fatigueA gentle reminder when signals have accumulated.
- Pomodoro as a modeClassic timing is available as one option.
Eye-load guidance aligns with NIOSH / CDC / OSHA. LogAgent is not medical advice: nudges are soft and signal-based.
How you worked with AI
A new layer: not where you coded, but how work with AI tools went — Claude Code, Cursor, Codex, console agents.
- Sessions and tokensHow much went to the agent dialog and what context it held.
- What landed in commitsLinking AI sessions to real repo changes.
- Review timeHow much reading and fixing generated code took.
Here "AI" is the object of analysis, not a neural net inside.
Money flows from the real day
Auto-grouping segments into projects, clients, and rates — with an invoice where every hour has a reason.
- Segments → projectsActivity maps itself to clients and rates.
- Invoice with rationale"Why this time is billable" next to each line.
- Explainable basisNot just "logged work" — show how it composed.
Value compounds over time
A day is thin context. The longer LogAgent stays, the more reliable the picture.
Read in the manifestoOne slice is not yet a pattern.
18% data confidenceYou see when focus peaks and dips.
42% data confidenceSwitches, breaks, fatigue — in motion.
70% data confidenceEnough data to trust conclusions.
100% data confidenceBuilt for people who live at the computer
LogAgent is made for people whose working time is the product. The more you switch between tasks, clients and tools, the faster it pays off.
- Freelancers
Honest hours, no manual timers
LogAgent splits your time across clients and projects on its own and rolls it into an invoice — no starting and stopping a stopwatch for every task.
Why it matters: A freelancer sells their time but can't recall where it went by the end of the day. Automatic, explainable tracking protects both income and client trust.
- Developers
Protect deep work
See how long deep work lasted and where switches broke it, plus AI coding sessions (Claude Code, Cursor) with tokens and cost, and git activity.
Why it matters: Constant interruptions and tool-hopping kill focus. Seeing it in numbers makes it easier to build the day around deep work and understand where AI time and budget go.
- Consultants
Precise per-client attribution
Time attaches to the right project and client automatically, and reports and invoices come together in a couple of clicks.
Why it matters: With many clients and projects, you need to show transparently what you billed for. Precise attribution removes disputes and saves hours of reporting.
- Remote teams
Productivity without surveillance
Personal day analytics, fatigue cues and smart breaks — data stays on the employee's device instead of going to a manager.
Why it matters: Remote work makes it easy to burn out or lose rhythm. LogAgent helps keep pace and protect yourself without becoming a surveillance tool.
At the intersection of privacy and automation
Most trackers force a trade-off: automatic capture in the cloud, or privacy at the cost of manual timers. LogAgent covers both corners at once.
Compared on typical capabilities per tool category; individual products vary. LogAgent sits where automation doesn't cost you privacy.
The problem lives between tools
Fatigue affects focus, focus affects quality, switches affect task duration, that affects billing. Five separate tools see fragments, not the picture.
LogAgent — first practical DuoHuman module
A personal self-digitization layer — personal life OS. LogAgent owns work state; more modules ahead.
Fair and local
14 days free, no card. Data stays on your device on every plan.
- Explainable day and analytics
- Local privacy, offline
- Context-aware breaks
- Everything in monthly
- Billing with rationale
- AI sessions: tokens, commits, review
- History and patterns over time
- All features, forever
- Current-version updates
- Build-in-public support
Early-stage product, built in public. Plans may be refined.
Try work state
Download LogAgent and see your first explainable day. Free, no card, all local.