You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Q:

Example of what SQLAlchemy can do, and Django ORM cannot

I've been doing a lot of research lately into using Pyramid with SQLAlchemy versus keeping a current application in Django.  That by itself is an entire debate, but I'm not here to discuss that.
What I do want to know is, why is SQLAlchemy universally considered better than Django ORM?  Almost every, if not every, comparison I've found between the two favors SQLAlchemy.  I assume performance is a big one, as the structure of SQLAlchemy lets it translate to SQL much more smoothly.
But, I've also heard that with harder tasks, Django ORM is nearly impossible to use.  I want to scope out how huge of an issue this can be.  I've been reading one of the reasons to switch to SQLAlchemy is when Django ORM is no longer suiting your needs.
So, in short, could someone provide a query (doesn't have to be actual SQL syntax) that SQLAlchemy can do, but Django ORM cannot possibly do without adding in additional raw SQL?
Update:
I've been noticing this question getting quite some attention since I first asked, so I'd like to throw in my extra two cents.
In the end we ended up using SQLAlchemy and I must say I'm happy with the decision.
I'm revisiting this question to provide an additional feature of SQLAlchemy that, so far, I've not been able to replicate in Django ORM.  If someone can provide an example of how to do this I'll gladly eat my words.
Let's say you want to use some postgresql function, such as similarity(), which provides a fuzzy comparison (see: Finding similar strings with PostgreSQL quickly - tl;dr input two strings get back a percent similarity).
I've done some searching on how to do this using the Django ORM and have found nothing other than using raw sql as seems to be apparent from their documentation: https://docs.djangoproject.com/en/dev/topics/db/sql/.
i.e.
Model.objects.raw('SELECT * FROM app_model ORDER BY \
similarity(name, %s) DESC;', [input_name])

SQLalchemy, however, has func(), as described here: http://docs.sqlalchemy.org/en/latest/core/sqlelement.html#sqlalchemy.sql.expression.func
from sqlalchemy import desc, func
session.query(Model).order_by(func.similarity(Model.name, input_name))

This allows you to generate sql for any defined sql/postgresql/etc function and not require raw sql.

A:

This is dangerously close to being non-constructive, but I'll bite.
Suppose we need to maintain inventories of certain items for a number of different, let's say, accounts. DDL follows:
CREATE TABLE account (
    id serial PRIMARY KEY,
    ...
);

CREATE TABLE item (
    id serial PRIMARY KEY,
    name text NOT NULL,
    ...
);

CREATE TABLE inventory (
    account_id integer NOT NULL REFERENCES account(id),
    item_id integer NOT NULL REFERENCES item(id),
    amount integer NOT NULL DEFAULT 0 CHECK (amount >= 0),
    PRIMARY KEY (account_id, item_id)
);

First of all, Django ORM can't work with composite primary keys. Yes, you can always add a surrogate key and unique constraint, but that's one more column and one more index than you actually need. For a big table with a small number of columns this would add noticeable size and performance overhead. Also, ORMs generally have trouble with identity mapping using anything other than primary key.
Now, let's say we want to query each item in the inventory of given account accompanied by its quantity, but also include all items not present there with quantity set to 0. And then sort this in descending order by quantity. Corresponding SQL:
SELECT item.id, item.name, ..., coalesce(inventory.amount, 0) AS amount
    FROM item LEFT OUTER JOIN inventory
        ON item.id = inventory.item_id AND inventory.team_id = ?
    ORDER BY amount DESC;

There is no way to express outer join with custom condition in Django ORM. Yes, you can make two simple separate queries and perform join by hand in Python loop. And performance probably won't suffer much in this particular case. But that's beside the point because results of every query could be reproduced on the application side using only basic SELECTs.
With SQLAlchemy:
class Account(Base):
    __tablename__ = 'account'
    id = Column(Integer, primary_key=True)
    ...

class Item(Base):
    __tablename__ = 'item'
    id = Column(Integer, primary_key=True)
    name = Column(String, nullable=False)
    ...

class Inventory(Base):
    __tablename__ = 'inventory'
    account_id = Column(Integer, ForeignKey('account.id'), primary_key=True,
            nullable=False)
    account = relationship(Account)
    item_id = Column(Integer, ForeignKey('item.id'), primary_key=True,
            nullable=False)
    item = relationship(Item)
    amount = Column(Integer, CheckConstraint('amount >= 0'), nullable=False,
            default=0)

account = session.query(Account).get(some_id)
result = (session
    .query(Item, func.coalesce(Inventory.amount, 0).label('amount'))
    .outerjoin(Inventory,
        and_(Item.id==Inventory.item_id, Inventory.account==account))
    .order_by(desc('amount'))
    .all())

As a side note, SQLAlchemy makes dictionary based collections very easy. With addition of the following code to the Account model you make relationship with Inventory appear as what it is: a mapping from items to their quantity.
items = relationship('Inventory',
    collection_class=attribute_mapped_collection('item_id'))
inventory = association_proxy('items', 'amount',
    creator=lambda k, v: Inventory(item_id=k, amount=v))

This enables you to write code such as:
account.inventory[item_id] += added_value

that transparently inserts or updates entries in the inventory table.
Complex joins, subqueries, window aggregates — Django ORM fails to deal with anything of that without falling back to raw SQL.

A:

This should work in Django 1.11:
inventory_amount = Subquery(account.inventory_set.filter(item=OuterRef('pk')).values('amount'))
Item.objects.annotate(inventory_amount=Coalesce(inventory_amount, Value(0)))