The mechanics of what constitute mindfulness however, need to be technically elucidated for a common audience. It's not too complicated if you have the details of memory management explained.
2024-01-19 at 11:42 am
aim lower
You should ask for things you want.
Others will ask for things they want.
Asking for things others want, and expecting others to ask for things you want, is a luxury.
Expecting luxuries to be baselines is folly.
Aim lower.
Distance and influence in the long run
I should spend a few years distancing myself from the common folk.
1983-2001 : near
2002-2004 : far
2005-2008 : near
2009-2012 : far
2013-2023 : near
2024- ?
2024-01-18 at 3:05 pm
Career strategies and biases
play at work
2024-01-17 at 9:51 pm
dumbing down abstract business ideas
department of cringe
Business realities
2024-01-15 at 6:38 pm
Desk Operations : dodging procrastination - note step 3
1. Reexamine universe of tasks.
2. Cluster tasks in useful dimensions.
[ [ 3. Clear working memory. ] ]
4. Load next cluster.
5. Resolve cluster.
6. Capture failed tasks.
7. GOTO 1.
Case Study. Bakery. B2C. COGS control
Case Study. A B2C bakery chain was looking for an AI/ML solution for cost savings, to reduce COGS.
Discussion :
Case Study. A B2C bakery chain was looking for an AI/ML solution for cost savings, to reduce COGS.
1.
The retail bakery business has the structural concern of needing display cases to look 75-90% full at all times.
This is solvable via (a) constant refilling of cases which is expensive and wasteful or (b) agile addition and removal of display cases, based on stock available for display ( the tiny shop approach ).
In summary at the retail merchandising / interior design / process design layer - the big shop should try to behave like a small shop. Because that is what is cost effective, per unit of revenue.
2.
The AI/ML bit for performance marketing is always possible - but it doesn't solve the cost problem, if there are too many cases to fill anyway.
With regards to the performance marketing aspect - it's just A/B testing on what people want to buy. There's an interpolated variable ... some products, with inelastic demand will sell even if their case looks mostly empty. Other products with elastic demand may be more sensitive to the empty case.
Main concern :
AI/ML solutions which are implemented to forecast sales and traffic based on external factors ... will simply not help as much. They can help, if other links in the value-chain are already optimised.
Household Solvents : toxicity & application
This is a very rough introduction, intended to disambiguate some common substances.
You can try the following solvents in sequence from weak to harsh; toxicity tends to increase with strength; you may also want to pre-test each one on the material to avoid damage:
///
/ RELATIVELY MIXED composition, LESS POLAR /
1. diesel ( C8-C21, evaporates slowly, oily )
2. kerosene ( C10-C16, evaporates less slowly, oily )
3. white spirit @ mineral spirit ( C7-C12 )
4. turpentine ( from trees ... but not less toxic )
5. heavy naphtha ( upstream, C7-C12 )
6. gasoline ( C4-C12, evaporates quickly )
/ RELATIVELY UNIFORM composition, MORE POLAR ( "paint thinner" is typically a mix of these )
1. ethanol / denatured alcohol
2. IPA @ isopropanol
3. xylene @ xylol ( aromatic hydrocarbon, non-polar, less toxic )
4. acetone / "nail-polish remover" ( ketone, polar, less toxic )
5. MEK @ methyl ethyl ketone @ butanone ( ketone, boils slower than acetone, more toxic )
6. toluene @ toluol ( aromatic hydrocarbon, non-polar, more toxic )