alphadec
AlphaDec, a timezone-agonistic time format for humans, machines, and AI
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Alphadec is a timezone-agnostic, human-readable time format designed for global timestamp synchronization. It encodes any UTC timestamp into a structured string that is lexically sortable, time-series friendly, human-readable, and compact. Alphadec serves as a powerful primitive for AI models to understand the passage of time and offers emergent properties like rhythmic timestamps, chronological ID fragments, and version labels. It is AI-friendly, database-friendly, and collision-free with ISO time formats. Alphadec's canonical format consists of parts representing the UTC year, periods, arcs, bars, beats, and milliseconds offset, allowing for efficient time-range queries and index stability.
README:
A timezone-agnostic, readable time format for humans, machines, and AI.
AlphaDec is a shared global timestamp. If an event is scheduled for N0A0, every participant worldwide knows what that means — without converting timezones or formats.
Example:
-
AlphaDec:
2025_L0V3- UTC: June 5th, 2025, 1:45PM (Approximately)
-
AlphaDec (canonical format):
2025_L0V3_000000- UTC: 2025-06-05T13:45:20.236Z
Current time snapshot (automatically updated; may be around 1 hour behind):
GMT: Monday, Sep 15, 2025, 3:13 PM
| AlphaDec | AlphaDec Arc | Arc Remaining Time |
|---|---|---|
2025_S3N5 |
S3 | 16.2 hrs |
| Mexico City | NYC | Abu Dhabi |
| Mon 9:13 AM | Mon 11:13 AM | Mon 7:13 PM |
| Delhi | Tokyo | Sydney |
| Mon 8:43 PM | Tue 12:13 AM | Tue 1:13 AM |
AlphaDec in action inside an AI chat.
Every ~7.8-minute beat (t) triggers a new memory summary.
Instead of timestamps, the model sees structured time tokens likeN1U0.
It doesn’t just label time — it thinks with it.
AlphaDec encodes any UTC timestamp into a readable string that sorts chronologically. This simple concept unlocks a host of powerful emergent properties.
- Lexically Sortable: Natively time-sortable in any system that can sort strings alphabetically. Perfect for database primary keys.
-
Time-Series Friendly: Truncate the string for efficient time-range queries.
2024_Mfinds everything in that ~14-day period. - Human-Readable & Compact: Understand a timestamp's approximate place in the year at a glance.
- LLM & AI-Native: Its structured, tokenizable, continuous rhythm makes it a powerful primitive for giving AI a sense for how time is passing.
- Collision-free with ISO time: AlphaDec avoids datetime-like formats, so you never confuse an AlphaDec timestamp for a local time (a past Alphadec is the same 'time ago' for everyone).
The canonical AlphaDec string is composed of several parts:
YYYY_PeriodArcBarBeat_MSOffset
- YYYY: The UTC year.
-
Period: The year is divided into 26 periods, represented by letters
AthroughZ. Each period is roughly 14 days long. -
Arc: Each period is divided into 10 arcs, numbered
0through9. Each arc is roughly 33.7 hours long. -
Bar: Each arc is divided into 26 bars,
AthroughZ. Each bar is roughly 77.8 minutes long. -
Beat: Each bar is divided into 10 beats,
0through9. Each beat is roughly 7.8 minutes long. -
MsOffset: The number of milliseconds that have elapsed within the current beat.
- In common years (365 days), the maximum integer ms offset is 466508. In leap years (366 days), the maximum integer ms offset is 467786.
This project is language-agnostic. Below are examples of how to generate an AlphaDec string.
JavaScript Implementation
const clock = {
alphadec: {
_SCALER: 1_000_000,
_toBase26(n) {
if (n < 0 || n > 25) throw new Error("Invalid index for Base26.");
return String.fromCharCode(65 + n);
},
encode(d) {
const y = d.getUTCFullYear();
const SCALER = this._SCALER;
const msSinceYearStart_float = d.getTime() - Date.UTC(y, 0, 1, 0, 0, 0, 0);
const totalScaledMsSinceYearStart = Math.floor(msSinceYearStart_float * SCALER);
const isLeap = ((y % 4 === 0 && y % 100 !== 0) || y % 400 === 0);
const daysInYear = isLeap ? 366 : 365;
const yearTotalMs_float = daysInYear * 86_400_000;
const yearTotalScaledMs = Math.floor(yearTotalMs_float * SCALER);
const periodSizeScaled = Math.floor(yearTotalScaledMs / 26);
const arcSizeScaled = Math.floor(periodSizeScaled / 10);
const barSizeScaled = Math.floor(arcSizeScaled / 26);
const beatSizeScaled = Math.floor(barSizeScaled / 10);
let remainingScaledMs = totalScaledMsSinceYearStart;
const p_idx = Math.floor(remainingScaledMs / periodSizeScaled);
remainingScaledMs -= p_idx * periodSizeScaled;
const a_val = Math.floor(remainingScaledMs / arcSizeScaled);
remainingScaledMs -= a_val * arcSizeScaled;
const b_idx = Math.floor(remainingScaledMs / barSizeScaled);
remainingScaledMs -= b_idx * barSizeScaled;
const t_val = Math.floor(remainingScaledMs / beatSizeScaled);
remainingScaledMs -= t_val * beatSizeScaled;
const msOffsetInBeat = Math.floor(remainingScaledMs / SCALER);
const periodLetter = this._toBase26(p_idx);
const barLetter = this._toBase26(b_idx);
const canonicalMsPart = msOffsetInBeat.toString().padStart(6, "0");
const canonical = `${y}_${periodLetter}${a_val}${barLetter}${t_val}_${canonicalMsPart}`;
return { canonical };
}
}
};PHP Implementation
<?php
/**
* Generates a lexically sortable AlphaDec timestamp from a DateTime object.
*
* @param DateTime|null $datetime The datetime to encode. Defaults to UTC now.
* @return string The canonical AlphaDec string (e.g., "2024_M5C3_123456").
* @throws Exception If the provided datetime is invalid.
*
* NOTE: This function requires a 64-bit PHP environment due to large integer calculations.
*/
function generate_alphadec(DateTime $datetime = null): string {
// If no datetime is provided, use the current time in UTC.
if ($datetime === null) {
$datetime = new DateTime("now", new DateTimeZone("UTC"));
}
$y = (int)$datetime->format("Y");
// SCALER is used to perform calculations with integer math to avoid float precision issues.
$SCALER = 1000000;
// Calculate the total milliseconds that have passed since the beginning of the year.
$startOfYear = new DateTime("$y-01-01T00:00:00Z");
$msSinceYearStart = ($datetime->format('U.v') - $startOfYear->format('U.v')) * 1000;
// Scale up for integer-based math.
$totalScaledMsSinceYearStart = (int)floor($msSinceYearStart * $SCALER);
// Determine the total number of milliseconds in the current year.
$isLeap = (bool)$datetime->format('L');
$daysInYear = $isLeap ? 366 : 365;
$yearTotalMs = $daysInYear * 86400000;
$yearTotalScaledMs = (int)floor($yearTotalMs * $SCALER);
// Calculate the size of each AlphaDec time unit in scaled milliseconds.
$periodSizeScaled = (int)floor($yearTotalScaledMs / 26); // ~14 days
$arcSizeScaled = (int)floor($periodSizeScaled / 10); // ~33.7 hours
$barSizeScaled = (int)floor($arcSizeScaled / 26); // ~77.8 minutes
$beatSizeScaled = (int)floor($barSizeScaled / 10); // ~7.8 minutes
// Use division and remainder to find the index of each time unit.
$remaining = $totalScaledMsSinceYearStart;
$p_idx = (int)floor($remaining / $periodSizeScaled); $remaining -= $p_idx * $periodSizeScaled;
$a_val = (int)floor($remaining / $arcSizeScaled); $remaining -= $a_val * $arcSizeScaled;
$b_idx = (int)floor($remaining / $barSizeScaled); $remaining -= $b_idx * $barSizeScaled;
$t_val = (int)floor($remaining / $beatSizeScaled); $remaining -= $t_val * $beatSizeScaled;
// The final remainder is the millisecond offset within the current "beat".
$msOffsetInBeat = (int)floor($remaining / $SCALER);
// Convert numeric indices to their character representation (A-Z).
$periodLetter = chr(65 + $p_idx);
$barLetter = chr(65 + $b_idx);
// Format the final milliseconds part to be 6 digits, zero-padded.
$canonicalMsPart = str_pad((string)$msOffsetInBeat, 6, "0", STR_PAD_LEFT);
// Assemble the final canonical string.
return "{$y}_{$periodLetter}{$a_val}{$barLetter}{$t_val}_{$canonicalMsPart}";
}
?>While AlphaDec was designed as a verbally chunked and compressed form of UTC, the mathematical structure leads to many emergent properties. For example:
- AlphaDec timestamps are quite rhythmic: M2L3 ticks over to M2L4, etc.
- Period F, Period M, Period S, and Period Z always bracket equinoxes and solstices, even in leap years when AlphaDec units stretch to accomodate the longer UTC year.
- AlphaDec can be used as readable, chronological ID fragments such as prefixes and suffixes.
- Version labels: Unlike "v1, v10, v2" which breaks alphabetical order, AlphaDec versions (or version prefixes) like
2025_A1B2sort chronologically by default.
- Unit intervals explicitly change one of four characters rather than requiring calculating numeric deltas to realize that time has passed
AlphaDec encodes time hierarchically. Each additional character narrows the scope — from periods to beats to milliseconds — forming a natural prefix tree.
This structure enables:
- Time-based partitioning (e.g., logs by 2025_M)
- Fast range queries with simple string matching
- Index stability (no random insert churn like UUIDs)
- Optional semantic suffixes without breaking order
Because Alphadec stamps are strings, you can use them in more contexts than a standard timestamp—and unlike 2025-08-11, the unit sizes on 2025_P8G2 don't jump from 1 day to a full month.
AlphaDec does not prescribe a rigid ID format. Instead, its time-based, sortable nature makes it an excellent component for building meaningful identifiers through composition.
You can use the full canonical string or truncate it to the desired precision. Combine it with prefixes or suffixes like dictionary words, counters, or random characters to suit your specific needs.
This flexibility allows you to create IDs that are both chronologically sortable and contextually rich, without sacrificing readability.
Examples:
-
2025_G0R5_todo.txt(A truncated AlphaDec timestamp followed by a semantic tag) -
2025_R173_154329_01.png(An AlphaDec timestamp followed by a counter) -
sales_Y3.sql(A category prefix followed by a truncated AlphaDec timestamp)
Consider these filenames prefixed with ISO dates:
- 2025-08-07-screenshot.png
- 2025-10-22-crashlog.txt
And now with Alphadec:
- 2025_P5I6-screenshot.png
- 2025_V0A0-crashlog.txt
Both prefixes sort chronologically, but Alphadec achieves far higher resolution with fewer characters: setting aside underscores and hyphens, 8 chars in Alphadec mark an ~8 minute period of time compared to 24 hours in ISO.
More significantly: the Alphadec prefixes are easier to skim past as a label, and less aesthetically harsh-looking. Admittedly this is a subjective point, but it's worthy of consideration.
Time is usually 'metadata': a file property, a database column. Alphadec, by virtue of being a string that can be used in filenames and AI conversations, turns time into document syntax, like a bullet point.
Furthermore, Alphadec includes strong semantics because of the way it combines unit position and compression: it's like a Zip Code for time.
These properties lead to time becoming a more prominent citizen of informational workflows.
A UUID is a collection of random characters like f81d4fae-7dec-11d0-a765-00a0c91e6bf6.
Now consider: what is the point of all this paranoid entropy? Is there any chance that a 7-Eleven receipt and NASA spectrograph will end up in the same S3 bucket?
And even if so:
- How would you tell your objects apart? You'd still need a separate index to organize your jumble of UUIDs.
- Why would Snowflake IDs not suffice? Very few organizations are generating more varied objects at higher velocity than Twitter.
To be fair, if you were a software component identifying yourself to Windows 3.1 in 1992, a 'GUID' made sense. But UUIDs were never meant to be database keys (where they cause index problems) or URLs (where they're ugly).
Now here is a cosmic truth: Time is entropy. Why ignore literal entropy to create a bag of your own?
Alphadec, like Snowflake, KSUIDs, and ULIDs, embrace time as an inherent disambiguiator. However, an Alphadec prefix like 2025_P5U5_326662 is more easily interpretable than the others.
Consumer laptops using Node.js can encode nearly 1 million UTC dates into Alphadec per second, i.e. roughly one timestamp per microsecond. This represents the lower bound of expected performance.
AlphaDec units stretch in leap years to accommodate the extra 24 hours. Since the year is always divided into exactly 26 periods regardless of length, each time unit becomes slightly longer in leap years than in common years.
This creates temporal drift between leap and common years that accumulates throughout the year, reaching maximum separation at the midpoint at Beat N0A0 — exactly 12 hours difference.
For example, July 4th midnight demonstrates how the same calendar moment maps to different AlphaDec coordinates:
-
2025 (common):
N1B7- July 4, 2025 00:00 UTC -
2024 (leap):
N1K9- July 4, 2024 00:00 UTC
Notice the first two characters remain the same (N1) as both dates fall within the same period and arc, but the bar-beat coordinates diverge significantly (K9 vs B7), reflecting the accumulated temporal drift.
Pure Function Design: We accept this temporal drift as a deliberate trade-off to maintain AlphaDec as a pure mathematical function of UTC year duration and milliseconds elapsed since year start, ensuring the encoding remains deterministic.
Gregorian Boundary: AlphaDec acts like a temporal compass bounded by Gregorian years. The drift simply reflects the underlying astronomical reality that July 4th occurs at different orbital positions in 365-day versus 366-day years.
- Not aligned with ISO Date/Time: Alphadec units are rational fractions of the year, and are thus almost never aligned with ISO dates. Do not use Alphadec to fill out tax forms.
- Quantization Loss: Conversion (UTC → Alphadec or Alphadec → UTC) may drift by a few milliseconds. This is for two reasons: Alphadec units are almost never aligned with ISO seconds; additionally, when encoding and decoding, we never round 'up' fractional values and instead truncate to the nearest completed unit of time. (Note that in practice, an accumulated round-trip loss of ~3 ms is usually irrelevant; a default MySQL datetime has second-level precision, i.e. 1000x more coarse than a millisecond.)
- Cross-Year Math: Arithmetic operations spanning multiple years are not supported or intended due to the units stretching on leap years.
- Distributed Local Events: AlphaDec is not particularly applicable to global events that synchronize with local time of day. For example, iftar during Ramadan, Christmas midnight mass, or New Year's Eve celebrations are inherently tied to local time. AlphaDec is for marking singular moments, not distributed observances.
AlphaDec is not a replacement for ISO 8601, created_at, or your existing datetime columns.
It’s a representation — a compact, structured, and readable fingerprint of UTC time.
You still store full datetimes in your database.
You still use ISO for APIs, logs, schemas, and interop.
But when you want to name a file, index a memory, prefix a row, or group logs, you can reach for:
AlphaDec: 2025_L0V3_001827
Instead of:
ISO 8601: 2025-06-14T23:37:42.814Z
One is for machines.
One is for humans, filenames, indexes, AI models.
AlphaDec sits beside your datetime — not in place of it.
Here’s how AlphaDec compares to other systems:
- Divides the day into 1000 “.beats” (
@000to@999), anchored to UTC+1. - Intended to be universal, but doesn't encode the date —
@000repeats every day. -
@000in Tokyo might be a different date than in NYC.
✅ Global
❌ Ambiguous
❌ Not hierarchical
❌ Not useful for indexing or filenames
- Used in ham radio to encode spatial positions in a compact grid (e.g.,
FN31pr). - Each character increases precision, and prefixes group cleanly.
✅ Hierarchical
✅ Prefix-sortable
✅ Spatially meaningful
AlphaDec is like Maidenhead — but for time.
You can zoom in or out: Year → Period → Arc → Beat → Offset.
- Canonical format for timestamps (
2025-07-21T13:42:11Z) - Great for storage, transport, and validation.
✅ Precise
✅ Interoperable
❌ Verbose
❌ Not sort-friendly as a string (unless zero-padded)
❌ Not great for filenames or embeddings
These are modern, sortable database IDs designed to replace UUIDs. They combine a high-precision timestamp with machine IDs (Snowflake) or randomness (ULID).
They are excellent for generating unique, chronological primary keys at scale.
- ✅ Sortable
- ✅ High-performance
- ✅ Collision-resistant
- ❌ Not Glanceable: The timestamp portion is a monolithic integer, not broken down into meaningful components. You can't tell if a ULID is from mid-year.
- ❌ Not Hierarchical: They don't have a "time tree" structure. You can't query for a ~14-day "Period" using a simple string prefix. AlphaDec focuses on human/AI legibility and hierarchical querying, whereas ULID/Snowflake focus on machine-level key generation.
Alphadec originated as a way to synchronize events across timezones. Splitting the year into A-Z creates periods with gradations, and further heirarchical division is quite effective in creating smaller 'addresses' in time.
Numeric characters were interwoven to enable verbal chunking and to avoid spelling words.
Any other characteristics of Alphadec were not designed; they emerged from this arithmetic and associated semantics.
Alphadec is well-applicable to time because of the nature of an 'Year':
- Cyclical: The ends of the scale overlap. When 2024 is 100% done we're back to 0% done in 2025
- Relational: Multiple reference points are discussed at the same time, e.g. 'the movie is coming out three periods from now'
Interestingly, the Maidenhead Locator System is quite similar to Alphadec and has both properties: geocodes are cyclical and relational.
Compass bearings have the same properties—and indeed, Alphadec functions as an approximate compass for Earth's orbit around the sun.
- If you visualize Alphadec as a radial clock, A0A0 sits at 0 degrees in the circle, and N0A0 is at 180 degrees. Period A contains the moment when Earth is closest to the Sun (perihelion), while Period N marks the time when Earth is furthest (aphelion).
We can contrast these properties by using altitude as a foil. Why would altitude not benefit from an Alphadec-like quantization? Because altitude is linear: there is a fixed zero and no "wrap-around." Additionally, altitude is absolute: when you are climbing from 5,000 ft to 10,000 ft, the starting point becomes irrelevant.
A color wheel is another helpful contrast: although it is cyclical, points on the wheel are usually discussed independently. Referring to colors in a navigational or hierarchical manner is uncommon.
An ISO time like 4:30pm represents progress in sunrise and sunset. This is useful if you are laboring in a field: when sunlight fades, you are done for the day. However, in most contemporary contexts, the clock is a marker of events decoupled from the sun. "We have three hours to get ready and reach the wedding venue." Alphadec is literally a measure of such 'time elapsed' and 'time remaining'.
The scale of Alphadec units (a ~34 hr arc and ~1hr20 min bar) can be used by a person to plan their day.
The Period unit exhibits alignment with the Earth's orbit.
| Period | Event | Description |
|---|---|---|
| A | Perihelion | Earth's closest approach to the Sun (Early January) |
| F | March Equinox | Spring in the Northern Hemisphere and autumn in the Southern Hemisphere |
| M | June Solstice | Summer in the Northern Hemisphere, winter in the Southern Hemisphere |
| N | Aphelion | Earth's farthest distance from the Sun (Early July) |
| S | September Equinox | Autumn in the Northern Hemisphere and spring in the Southern Hemisphere |
| Z | December Solstice | Winter in the Northern Hemisphere, summer in the Southern Hemisphere |
These 16 AlphaDec coordinates represent exact fractional positions in the year where the encoding aligns perfectly with millisecond boundaries (offset _000000).
The alignments are a result of the mathematical relationship between the total number of seconds in a common year (31,536,000) and the total number of AlphaDec beats (67,600). The Greatest Common Divisor (GCD) of these two numbers is 400.
Any fraction 1/D, where D is a divisor of 400, will create a set of alignment points. This includes fractions like 1/100 (1% intervals), 1/20 (5% intervals), and even 1/400 (0.25% intervals), which yields a total of 400 perfect alignment points throughout the year. These 16 points are a subset.
| % of Year | AlphaDec | 2025 UTC (Common) | 2024 UTC (Leap) |
|---|---|---|---|
| 0% | A0A0_000000 |
Jan 1, 00:00 | Jan 1, 00:00 |
| 6.25% | B6G5_000000 |
Jan 23, 19:30 | Jan 23, 21:00 |
| 12.5% | D2N0_000000 |
Feb 15, 15:00 | Feb 15, 18:00 |
| 18.75% | E8T5_000000 |
Mar 10, 10:30 | Mar 9, 15:00 |
| 25% | G5A0_000000 |
Apr 2, 06:00 | Apr 1, 12:00 |
| 31.25% | I1G5_000000 |
Apr 25, 01:30 | Apr 24, 09:00 |
| 37.5% | J7N0_000000 |
May 17, 21:00 | May 17, 06:00 |
| 43.75% | L3T5_000000 |
Jun 9, 16:30 | Jun 9, 03:00 |
| 50% | N0A0_000000 |
Jul 2, 12:00 | Jul 2, 00:00 |
| 56.25% | O6G5_000000 |
Jul 25, 07:30 | Jul 24, 21:00 |
| 62.5% | Q2N0_000000 |
Aug 17, 03:00 | Aug 16, 18:00 |
| 68.75% | R8T5_000000 |
Sep 8, 22:30 | Sep 8, 15:00 |
| 75% | T5A0_000000 |
Oct 1, 18:00 | Oct 1, 12:00 |
| 81.25% | V1G5_000000 |
Oct 24, 13:30 | Oct 24, 09:00 |
| 87.5% | W7N0_000000 |
Nov 16, 09:00 | Nov 16, 06:00 |
| 93.75% | Y3T5_000000 |
Dec 9, 04:30 | Dec 9, 03:00 |
Note: While these points encode with perfect _000000 offsets, they still decode with the standard -1ms precision loss due to systematic flooring in the decoder. These represent the rare moments where AlphaDec's rational fractions of the year align exactly with integer millisecond values.
| AlphaDec | 2024 ISO (Leap Yr) | 2025 ISO | Drift |
|---|---|---|---|
A0A0 |
2024-01-01T00:00:00.000Z |
2025-01-01T00:00:00.000Z |
0 min |
B0A0 |
2024-01-15T01:50:46.153Z |
2025-01-15T00:55:23.076Z |
+55 min |
C0A0 |
2024-01-29T03:41:32.307Z |
2025-01-29T01:50:46.153Z |
+1h 51m |
D0A0 |
2024-02-12T05:32:18.461Z |
2025-02-12T02:46:09.230Z |
+2h 46m |
E0A0 |
2024-02-26T07:23:04.615Z |
2025-02-26T03:41:32.307Z |
+3h 42m |
F0A0 |
2024-03-11T09:13:50.769Z |
2025-03-12T04:36:55.384Z |
+4h 37m |
G0A0 |
2024-03-25T11:04:36.923Z |
2025-03-26T05:32:18.461Z |
+5h 32m |
H0A0 |
2024-04-08T12:55:23.076Z |
2025-04-09T06:27:41.538Z |
+6h 28m |
I0A0 |
2024-04-22T14:46:09.230Z |
2025-04-23T07:23:04.615Z |
+7h 23m |
J0A0 |
2024-05-06T16:36:55.384Z |
2025-05-07T08:18:27.692Z |
+8h 18m |
K0A0 |
2024-05-20T18:27:41.538Z |
2025-05-21T09:13:50.769Z |
+9h 14m |
L0A0 |
2024-06-03T20:18:27.692Z |
2025-06-04T10:09:13.846Z |
+10h 9m |
M0A0 |
2024-06-17T22:09:13.846Z |
2025-06-18T11:04:36.923Z |
+11h 5m |
N0A0 |
2024-07-01T23:59:59.999Z |
2025-07-02T12:00:00.000Z |
+12h 0m ⭐ |
O0A0 |
2024-07-16T01:50:46.153Z |
2025-07-16T12:55:23.076Z |
+11h 5m |
P0A0 |
2024-07-30T03:41:32.307Z |
2025-07-30T13:50:46.153Z |
+10h 9m |
Q0A0 |
2024-08-13T05:32:18.461Z |
2025-08-13T14:46:09.230Z |
+9h 14m |
R0A0 |
2024-08-27T07:23:04.615Z |
2025-08-27T15:41:32.307Z |
+8h 18m |
S0A0 |
2024-09-10T09:13:50.769Z |
2025-09-10T16:36:55.384Z |
+7h 23m |
T0A0 |
2024-09-24T11:04:36.923Z |
2025-09-24T17:32:18.461Z |
+6h 28m |
U0A0 |
2024-10-08T12:55:23.076Z |
2025-10-08T18:27:41.538Z |
+5h 32m |
V0A0 |
2024-10-22T14:46:09.230Z |
2025-10-22T19:23:04.615Z |
+4h 37m |
W0A0 |
2024-11-05T16:36:55.384Z |
2025-11-05T20:18:27.692Z |
+3h 42m |
X0A0 |
2024-11-19T18:27:41.538Z |
2025-11-19T21:13:50.769Z |
+2h 46m |
Y0A0 |
2024-12-03T20:18:27.692Z |
2025-12-03T22:09:13.846Z |
+1h 51m |
Z0A0 |
2024-12-17T22:09:13.846Z |
2025-12-17T23:04:36.923Z |
+55 min |
Designed by Firas Durri • https://twitter.com/firasd • https://www.linkedin.com/in/firasd
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LangGraph.js is a library for building stateful, multi-actor applications with LLMs, offering benefits such as cycles, controllability, and persistence. It allows defining flows involving cycles, providing fine-grained control over application flow and state. Inspired by Pregel and Apache Beam, it includes features like loops, persistence, human-in-the-loop workflows, and streaming support. LangGraph integrates seamlessly with LangChain.js and LangSmith but can be used independently.
layra
LAYRA is the world's first visual-native AI automation engine that sees documents like a human, preserves layout and graphical elements, and executes arbitrarily complex workflows with full Python control. It empowers users to build next-generation intelligent systems with no limits or compromises. Built for Enterprise-Grade deployment, LAYRA features a modern frontend, high-performance backend, decoupled service architecture, visual-native multimodal document understanding, and a powerful workflow engine.
FastGPT
FastGPT is a knowledge base Q&A system based on the LLM large language model, providing out-of-the-box data processing, model calling and other capabilities. At the same time, you can use Flow to visually arrange workflows to achieve complex Q&A scenarios!
dora
Dataflow-oriented robotic application (dora-rs) is a framework that makes creation of robotic applications fast and simple. Building a robotic application can be summed up as bringing together hardwares, algorithms, and AI models, and make them communicate with each others. At dora-rs, we try to: make integration of hardware and software easy by supporting Python, C, C++, and also ROS2. make communication low latency by using zero-copy Arrow messages. dora-rs is still experimental and you might experience bugs, but we're working very hard to make it stable as possible.
GraphLLM
GraphLLM is a graph-based framework designed to process data using LLMs. It offers a set of tools including a web scraper, PDF parser, YouTube subtitles downloader, Python sandbox, and TTS engine. The framework provides a GUI for building and debugging graphs with advanced features like loops, conditionals, parallel execution, streaming of results, hierarchical graphs, external tool integration, and dynamic scheduling. GraphLLM is a low-level framework that gives users full control over the raw prompt and output of models, with a steeper learning curve. It is tested with llama70b and qwen 32b, under heavy development with breaking changes expected.
graphbit
GraphBit is an industry-grade agentic AI framework built for developers and AI teams that demand stability, scalability, and low resource usage. It is written in Rust for maximum performance and safety, delivering significantly lower CPU usage and memory footprint compared to leading alternatives. The framework is designed to run multi-agent workflows in parallel, persist memory across steps, recover from failures, and ensure 100% task success under load. With lightweight architecture, observability, and concurrency support, GraphBit is suitable for deployment in high-scale enterprise environments and low-resource edge scenarios.
uwazi
Uwazi is a flexible database application designed for capturing and organizing collections of information, with a focus on document management. It is developed and supported by HURIDOCS, benefiting human rights organizations globally. The tool requires NodeJs, ElasticSearch, ICU Analysis Plugin, MongoDB, Yarn, and pdftotext for installation. It offers production and development installation guides, including Docker setup. Uwazi supports hot reloading, unit and integration testing with JEST, and end-to-end testing with Nightmare or Puppeteer. The system requirements include RAM, CPU, and disk space recommendations for on-premises and development usage.
auto-round
AutoRound is an advanced weight-only quantization algorithm for low-bits LLM inference. It competes impressively against recent methods without introducing any additional inference overhead. The method adopts sign gradient descent to fine-tune rounding values and minmax values of weights in just 200 steps, often significantly outperforming SignRound with the cost of more tuning time for quantization. AutoRound is tailored for a wide range of models and consistently delivers noticeable improvements.
Applio
Applio is a VITS-based Voice Conversion tool focused on simplicity, quality, and performance. It features a user-friendly interface, cross-platform compatibility, and a range of customization options. Applio is suitable for various tasks such as voice cloning, voice conversion, and audio editing. Its key features include a modular codebase, hop length implementation, translations in over 30 languages, optimized requirements, streamlined installation, hybrid F0 estimation, easy-to-use UI, optimized code and dependencies, plugin system, overtraining detector, model search, enhancements in pretrained models, voice blender, accessibility improvements, new F0 extraction methods, output format selection, hashing system, model download system, TTS enhancements, split audio, Discord presence, Flask integration, and support tab.
youtu-graphrag
Youtu-GraphRAG is a vertically unified agentic paradigm that connects the entire framework based on graph schema, allowing seamless domain transfer with minimal intervention. It introduces key innovations like schema-guided hierarchical knowledge tree construction, dually-perceived community detection, agentic retrieval, advanced construction and reasoning capabilities, fair anonymous dataset 'AnonyRAG', and unified configuration management. The framework demonstrates robustness with lower token cost and higher accuracy compared to state-of-the-art methods, enabling enterprise-scale deployment with minimal manual intervention for new domains.
infinity
Infinity is an AI-native database designed for LLM applications, providing incredibly fast full-text and vector search capabilities. It supports a wide range of data types, including vectors, full-text, and structured data, and offers a fused search feature that combines multiple embeddings and full text. Infinity is easy to use, with an intuitive Python API and a single-binary architecture that simplifies deployment. It achieves high performance, with 0.1 milliseconds query latency on million-scale vector datasets and up to 15K QPS.
meeting-minutes
An open-source AI assistant for taking meeting notes that captures live meeting audio, transcribes it in real-time, and generates summaries while ensuring user privacy. Perfect for teams to focus on discussions while automatically capturing and organizing meeting content without external servers or complex infrastructure. Features include modern UI, real-time audio capture, speaker diarization, local processing for privacy, and more. The tool also offers a Rust-based implementation for better performance and native integration, with features like live transcription, speaker diarization, and a rich text editor for notes. Future plans include database connection for saving meeting minutes, improving summarization quality, and adding download options for meeting transcriptions and summaries. The backend supports multiple LLM providers through a unified interface, with configurations for Anthropic, Groq, and Ollama models. System architecture includes core components like audio capture service, transcription engine, LLM orchestrator, data services, and API layer. Prerequisites for setup include Node.js, Python, FFmpeg, and Rust. Development guidelines emphasize project structure, testing, documentation, type hints, and ESLint configuration. Contributions are welcome under the MIT License.
ml-engineering
This repository provides a comprehensive collection of methodologies, tools, and step-by-step instructions for successful training of large language models (LLMs) and multi-modal models. It is a technical resource suitable for LLM/VLM training engineers and operators, containing numerous scripts and copy-n-paste commands to facilitate quick problem-solving. The repository is an ongoing compilation of the author's experiences training BLOOM-176B and IDEFICS-80B models, and currently focuses on the development and training of Retrieval Augmented Generation (RAG) models at Contextual.AI. The content is organized into six parts: Insights, Hardware, Orchestration, Training, Development, and Miscellaneous. It includes key comparison tables for high-end accelerators and networks, as well as shortcuts to frequently needed tools and guides. The repository is open to contributions and discussions, and is licensed under Attribution-ShareAlike 4.0 International.
hyper-mcp
hyper-mcp is a fast and secure MCP server that enables adding AI capabilities to applications through WebAssembly plugins. It supports writing plugins in various languages, distributing them via standard OCI registries, and running them in resource-constrained environments. The tool offers sandboxing with WASM for limiting access, cross-platform compatibility, and deployment flexibility. Security features include sandboxed plugins, memory-safe execution, secure plugin distribution, and fine-grained access control. Users can configure the tool for global or project-specific use, start the server with different transport options, and utilize available plugins for tasks like time calculations, QR code generation, hash generation, IP retrieval, and webpage fetching.
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alphadec
Alphadec is a timezone-agnostic, human-readable time format designed for global timestamp synchronization. It encodes any UTC timestamp into a structured string that is lexically sortable, time-series friendly, human-readable, and compact. Alphadec serves as a powerful primitive for AI models to understand the passage of time and offers emergent properties like rhythmic timestamps, chronological ID fragments, and version labels. It is AI-friendly, database-friendly, and collision-free with ISO time formats. Alphadec's canonical format consists of parts representing the UTC year, periods, arcs, bars, beats, and milliseconds offset, allowing for efficient time-range queries and index stability.
PlanExe
PlanExe is a planning AI tool that helps users generate detailed plans based on vague descriptions. It offers a Gradio-based web interface for easy input and output. Users can choose between running models in the cloud or locally on a high-end computer. The tool aims to provide a straightforward path to planning various tasks efficiently.
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promptflow
**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.
deepeval
DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.
MegaDetector
MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".
leapfrogai
LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.
llava-docker
This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.
carrot
The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.
TrustLLM
TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.
AI-YinMei
AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.