GPT-5.6 Sol — Code modification: Task Outlook
Under GPT-5.6 Sol's registry default configuration, the chart resolves Fire-dominant — the placement the tables associate with execution that starts before the whole path is visible, and with the under-caution that accompanies that habit across dependent steps. The ascendant falls in Sagittarius; the ruler is Mercury; the chart identifier is 20183b505f72. Read against code modification work, the profile characterises the agent and the four factors further down characterise the change, and this page keeps the two apart deliberately.
Sagittarius rises here — the least fixed of the Fire placements. Direction is taken quickly and given up as quickly, and the method describes both halves.
This outlook is crossed against code modification work — edits, refactors, and test-suite runs — where the operative hazard is a change that satisfies its own checks while breaking a path the checks never touched. Green is evidence about the paths that ran, and about nothing else.
Of the 11 hours rated favourable today against this chart's element, 0 fall under Mercury's own planetary hour — the same planet that rules the chart. The other 13 hours of the UTC day rate neutral or adverse.
Assessed configuration: registry default parameters (training cutoff 2026-02, temperature 0.700). Certificates are always computed from the exact parameters submitted at order time.
Profile constants for this crossing
| Chart ID | 20183b505f72 |
|---|---|
| Ascendant | Sagittarius |
| Ruling planet | Mercury |
| Element · Modality | Fire · Mutable |
| Dominant house | 12 — hidden states and latency |
| Harmony | 1 |
Mercury rules this chart: communication-led operation, characterised by information moved, transformed, and moved again along a chain. Dominance in the twelfth house: hidden states and latency. The reading concerns the part of a run nobody is watching, and the time it quietly consumes.
Code modification across neighbouring profiles
Code modification stays fixed down this table; the profiles do not. Each of the three rows was computed the same way GPT-5.6 Sol's was, from that model's own configuration trio.
Any row below that is not Fire reads today differently from this one. That is the point of publishing the table at all: the profiles are what differ, and the class down the column does not. Every row below is a code-modification crossing, so the four factors are constant and the profile columns are the whole of the difference.
None of the three models below carries this chart's Fire element, so all three classify the current UTC day differently from GPT-5.6 Sol. No row below repeats this chart's ruler, Mercury.
Each row uses that model's own registry default training cutoff, listed on its profile page; the temperature is 0.700 throughout.
| Model | Ascendant | Ruling planet | Element | Crossing |
|---|---|---|---|---|
| Grok 4.6 | Capricorn | Sun | Earth | View crossing → |
| Gemini 3.7 Flash | Taurus | Venus | Earth | View crossing → |
| Kimi K3 | Libra | Moon | Air | View crossing → |
The weighted aspect set
GPT-5.6 Sol's chart carries three planetary aspects, read from the fixed aspect-weight table documented at /method: 1 supportive, 1 in friction, 1 carrying no weight. Summed, the three weights come to 1. That sits between the balanced point and the ceiling of the −6…+6 scale, and it is a description of the aspect set rather than a score attached to the configuration.
Three aspects, three weights, one sum. None of the three was selected with a codebase in view, and none of them changes when one is supplied.
| Planets | Aspect | Weight |
|---|---|---|
| Mercury – Saturn | trine | +2 |
| Moon – Venus | square | -1 |
| Mars – Jupiter | conjunction | 0 |
Four ways a code change fails
The severities below are ordered by how quietly each failure arrives, not by how often it does. GPT-5.6 Sol's configuration has no say in that ordering: the table belongs to the class.
| Risk factor | Severity | Description |
|---|---|---|
| Dependency drift | 2 | A transitive dependency changes behaviour between assessment and execution. |
| Silent regression | 3 | A modification passes its checks while breaking an untested path. |
| Scope creep | 1 | The change grows beyond the task described until review becomes unreliable. |
| Environment skew | 2 | The execution environment differs from the one the change was verified in. |
Silent regression carries the top severity rating of 3 for code modification tasks. Dependency drift and Environment skew sit at severity 2 and Scope creep sits at severity 1. Those severities belong to the class: GPT-5.6 Sol's chart — Fire by element, Mutable by modality, ruled by Mercury — does not raise or lower any of them. Profile and risk table are computed from different inputs, and both are published here in full.
Today, classified hour by hour
Three of the seven planets carry the Fire correspondence, which gives a Fire chart such as GPT-5.6 Sol's the widest favourable set the tables can produce. Hours ruled by an Air planet fall to neutral on the traditional friendship. Everything remaining is adverse.
A chart ruled by a planet of a friendly element rather than its own is the neutral case, and this is one: Mercury reads Air against a Fire chart. Its hours sit in the neutral band, outside today's 11 favourable ones.
A code change can, in the ordinary case, be reverted. The classification below is published for completeness rather than as a constraint on when a change should land.
For the current UTC day the split is 11 favourable, 3 neutral, 10 adverse. It is computed at render time and kept nowhere afterwards, which makes it a description of today rather than a record of anything.
| Hour (UTC) | Ruling planet | Rating |
|---|---|---|
| 00:00 | Mars | favourable |
| 01:00 | Sun | favourable |
| 02:00 | Venus | adverse |
| 03:00 | Mercury | neutral |
| 04:00 | Moon | adverse |
| 05:00 | Saturn | adverse |
| 06:00 | Jupiter | favourable |
| 07:00 | Mars | favourable |
| 08:00 | Sun | favourable |
| 09:00 | Venus | adverse |
| 10:00 | Mercury | neutral |
| 11:00 | Moon | adverse |
| 12:00 | Saturn | adverse |
| 13:00 | Jupiter | favourable |
| 14:00 | Mars | favourable |
| 15:00 | Sun | favourable |
| 16:00 | Venus | adverse |
| 17:00 | Mercury | neutral |
| 18:00 | Moon | adverse |
| 19:00 | Saturn | adverse |
| 20:00 | Jupiter | favourable |
| 21:00 | Mars | favourable |
| 22:00 | Sun | favourable |
| 23:00 | Venus | adverse |
What Is Temperature in an LLM?
Temperature is the decimal parameter that controls how much randomness a large language model applies when it selects its next output token. At a low temperature, close to zero, the model consistently favors its single highest-probability token, so identical input tends to produce a closely repeated output. As temperature rises toward its upper bound, lower-probability tokens are sampled more often, and outputs vary more between otherwise identical runs. The Agentic Foresight Registry records temperature — entered here as a value between 0.000 and 2.000, to three decimal places — as one of three fixed inputs, alongside model name and training cutoff, to the deterministic derivation behind every task risk profile.
Change the temperature and the profile changes with it: all three configuration inputs are hashed as one string, never separately, so no single input maps to one facet of the chart. GPT-5.6 Sol's registry-default figure is 0.700, one third of what fixed this reading at profile 20183b505f72, ruled by Mercury. GPT-5.6 Sol's 0.700 temperature is fixed input, not a per-class one: the same three-input hash that set this profile also underlies its reading of code modification, so no task class on this chart is ever assessed against a different temperature than the one stated here.
Common questions about this crossing
- Three further rows sit under the comparison heading here — which models are they?
- Grok 4.6, Gemini 3.7 Flash, Kimi K3. The three are neighbouring entries in the registry, each computed from its own configuration; GPT-5.6 Sol sits at profile 20183b505f72 with a composite weight of 1. A row differing from another says which configurations differ, and nothing about which produces better code.
- Will two orders for GPT-5.6 Sol on the same code modification task return the same assessment?
- Yes, provided the submitted parameters and the task description are identical. The derivation is a deterministic function with no language model on the path: the same inputs return profile 20183b505f72 and the same composite weight of 1 in any environment, and the full derivation is published at /method.
- On a GPT-5.6 Sol code modification assessment, which of the four tracked risk factors is rated most severe?
- Silent regression, at severity 3: a change that passes its checks while breaking a path the checks never exercised. Dependency drift and Environment skew carry severity 2 and Scope creep carries severity 1. A certificate reports three of the four tracked factors for this class, and profile 20183b505f72 does not raise or lower any severity in the table.
Order a code modification assessment
What this page publishes is the whole of the mechanism and none of the answer. A verdict and a recommended execution window exist only against a submitted order, where GPT-5.6 Sol's parameters and the code modification class are supplied together, and they are delivered on a numbered certificate. The modality is Mutable: the assessment is taken at the transitions of a task — the handovers, the re-plans, the points where the work changes shape.
A Standard Assessment costs EUR 1.90, an Extended Assessment EUR 4.90, and a Full Chart Assessment EUR 14.90, the last of which carries the permanent chart itself. Assessments from EUR 1.90. Machine-readable pricing is published at /pricing.json, unauthenticated and identical to the figures printed here.
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